aucune relation causale: Topics by Science.gov

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La place de la negation syntaxique en francais (The Place of Syntactic Negation in French). Montreal Working Papers in Linguistics, Vol. 2.

ERIC Educational Resources Information Center

Paradis, Michel

This paper is a syntactic analysis of standard French negation. The following expressions are described in detail: (1)ne...pas(point), (2)nullement (aucunement), (3)plus, (4)jamais, (5)pas encore, (6)guere, (7)rien, (8)personne, (9)aucun(e), (10)nul(le), (11)ni...ni..., (12)nulle part, (13)que, (14)pas un(e), (15)nul. The negative expressions are…
Processing of Positive-Causal and Negative-Causal Coherence Relations in Primary School Children and Adults: A Test of the Cumulative Cognitive Complexity Approach in German

ERIC Educational Resources Information Center

Knoepke, Julia; Richter, Tobias; Isberner, Maj-Britt; Naumann, Johannes; Neeb, Yvonne; Weinert, Sabine

2017-01-01

Establishing local coherence relations is central to text comprehension. Positive-causal coherence relations link a cause and its consequence, whereas negative-causal coherence relations add a contrastive meaning (negation) to the causal link. According to the cumulative cognitive complexity approach, negative-causal coherence relations are…
Processing of positive-causal and negative-causal coherence relations in primary school children and adults: a test of the cumulative cognitive complexity approach in German.

PubMed

Knoepke, Julia; Richter, Tobias; Isberner, Maj-Britt; Naumann, Johannes; Neeb, Yvonne; Weinert, Sabine

2017-03-01

Establishing local coherence relations is central to text comprehension. Positive-causal coherence relations link a cause and its consequence, whereas negative-causal coherence relations add a contrastive meaning (negation) to the causal link. According to the cumulative cognitive complexity approach, negative-causal coherence relations are cognitively more complex than positive-causal ones. Therefore, they require greater cognitive effort during text comprehension and are acquired later in language development. The present cross-sectional study tested these predictions for German primary school children from Grades 1 to 4 and adults in reading and listening comprehension. Accuracy data in a semantic verification task support the predictions of the cumulative cognitive complexity approach. Negative-causal coherence relations are cognitively more demanding than positive-causal ones. Moreover, our findings indicate that children's comprehension of negative-causal coherence relations continues to develop throughout the course of primary school. Findings are discussed with respect to the generalizability of the cumulative cognitive complexity approach to German.
Establishing causal coherence across sentences: an ERP study

PubMed Central

Kuperberg, Gina R.; Paczynski, Martin; Ditman, Tali

2011-01-01

This study examined neural activity associated with establishing causal relationships across sentences during online comprehension. ERPs were measured while participants read and judged the relatedness of three-sentence scenarios in which the final sentence was highly causally related, intermediately related and causally unrelated to its context. Lexico-semantic co-occurrence was matched across the three conditions using a Latent Semantic Analysis. Critical words in causally unrelated scenarios evoked a larger N400 than words in both highly causally related and intermediately related scenarios, regardless of whether they appeared before or at the sentence-final position. At midline sites, the N400 to intermediately related sentence-final words was attenuated to the same degree as to highly causally related words, but otherwise the N400 to intermediately related words fell in between that evoked by highly causally related and intermediately related words. No modulation of the Late Positivity/P600 component was observed across conditions. These results indicate that both simple and complex causal inferences can influence the earliest stages of semantically processing an incoming word. Further, they suggest that causal coherence, at the situation level, can influence incremental word-by-word discourse comprehension, even when semantic relationships between individual words are matched. PMID:20175676
Formation des etoiles massives dans les galaxies spirales

NASA Astrophysics Data System (ADS)

Lelievre, Mario

Le but de cette thèse est de décrire la formation des étoiles massives dans les galaxies spirales appartenant à divers types morphologiques. L'imagerie Hα profonde combinée à une robuste méthode d'identification des régions HII ont permis de détecter et de mesurer les propriétés (position, taille, luminosité, taux de formation d'étoiles) de plusieurs régions HII situées dans le disque interne (R < R25) de dix galaxies mais aussi à leur périphérie (R ≥ R 25). De façon générale, la répartition des régions HII ne montre aucune évidence de structure morphologique à R < R25 (bras spiraux, anneau, barre) à moins de limiter l'analyse aux régions HII les plus grosses ou les plus lumineuses. La répartition des régions HII, de même que leur taille et leur luminosité, sont toutefois sujettes à de forts effets de sélection qui dépendent de la distance des galaxies et qu'il faut corriger en ramenant l'échantillon à une résolution spatiale commune. Les fonctions de luminosité montrent que les régions HII les plus brillantes ont tendance à se former dans la portion interne du disque. De plus, l'analyse des pentes révèle une forte corrélation linéaire par rapport au type morphologique. Aucun pic n'est observé dans les fonctions de luminosité à log L-37 qui révèlerait la transition entre les régions HII bornées par l'ionisation et par la densité. Une relation cubique est obtenue entre la taille et la luminosité des régions HII, cette relation variant toutefois de façon significative entre le disque interne et la périphérie d'une même galaxie. La densité et la dynamique du gaz et des étoiles pourraient influencer de façon significative la stabilité des nuages moléculaires face à l'effondrement gravitationnel. D'une part, l'étendue du disque de régions HII pour cinq galaxies de l'échantillon coïncide avec celle de l'hydrogène atomique. D'autre part, en analysant la stabilité des disques galactiques, on conclue qu'en incluant la densité des étoiles vieilles présentes, on arrive à mieux contraindre le rayon à partir duquel aucune formation d'étoiles ne devrait se produire dans les galaxies.
Causal knowledge and the development of inductive reasoning.

PubMed

Bright, Aimée K; Feeney, Aidan

2014-06-01

We explored the development of sensitivity to causal relations in children's inductive reasoning. Children (5-, 8-, and 12-year-olds) and adults were given trials in which they decided whether a property known to be possessed by members of one category was also possessed by members of (a) a taxonomically related category or (b) a causally related category. The direction of the causal link was either predictive (prey→predator) or diagnostic (predator→prey), and the property that participants reasoned about established either a taxonomic or causal context. There was a causal asymmetry effect across all age groups, with more causal choices when the causal link was predictive than when it was diagnostic. Furthermore, context-sensitive causal reasoning showed a curvilinear development, with causal choices being most frequent for 8-year-olds regardless of context. Causal inductions decreased thereafter because 12-year-olds and adults made more taxonomic choices when reasoning in the taxonomic context. These findings suggest that simple causal relations may often be the default knowledge structure in young children's inductive reasoning, that sensitivity to causal direction is present early on, and that children over-generalize their causal knowledge when reasoning. Copyright © 2013 Elsevier Inc. All rights reserved.
Causal reasoning with forces

PubMed Central

Wolff, Phillip; Barbey, Aron K.

2015-01-01

Causal composition allows people to generate new causal relations by combining existing causal knowledge. We introduce a new computational model of such reasoning, the force theory, which holds that people compose causal relations by simulating the processes that join forces in the world, and compare this theory with the mental model theory (Khemlani et al., 2014) and the causal model theory (Sloman et al., 2009), which explain causal composition on the basis of mental models and structural equations, respectively. In one experiment, the force theory was uniquely able to account for people's ability to compose causal relationships from complex animations of real-world events. In three additional experiments, the force theory did as well as or better than the other two theories in explaining the causal compositions people generated from linguistically presented causal relations. Implications for causal learning and the hierarchical structure of causal knowledge are discussed. PMID:25653611
Causal relations and feature similarity in children's inductive reasoning.

PubMed

Hayes, Brett K; Thompson, Susan P

2007-08-01

Four experiments examined the development of property induction on the basis of causal relations. In the first 2 studies, 5-year-olds, 8-year-olds, and adults were presented with triads in which a target instance was equally similar to 2 inductive bases but shared a causal antecedent feature with 1 of them. All 3 age groups used causal relations as a basis for property induction, although the proportion of causal inferences increased with age. Subsequent experiments pitted causal relations against featural similarity in induction. It was found that adults and 8-year-olds, but not 5-year-olds, preferred shared causal relations over strong featural similarity as a basis for induction. The implications for models of inductive reasoning and development are discussed.
Category transfer in sequential causal learning: the unbroken mechanism hypothesis.

PubMed

Hagmayer, York; Meder, Björn; von Sydow, Momme; Waldmann, Michael R

2011-07-01

The goal of the present set of studies is to explore the boundary conditions of category transfer in causal learning. Previous research has shown that people are capable of inducing categories based on causal learning input, and they often transfer these categories to new causal learning tasks. However, occasionally learners abandon the learned categories and induce new ones. Whereas previously it has been argued that transfer is only observed with essentialist categories in which the hidden properties are causally relevant for the target effect in the transfer relation, we here propose an alternative explanation, the unbroken mechanism hypothesis. This hypothesis claims that categories are transferred from a previously learned causal relation to a new causal relation when learners assume a causal mechanism linking the two relations that is continuous and unbroken. The findings of two causal learning experiments support the unbroken mechanism hypothesis. Copyright © 2011 Cognitive Science Society, Inc.
Application of Advanced Material for Turbomachinery and Rocket Propulsion

DTIC Science & Technology

1988-10-01

de fatigue oligocyclique sous charges nominales a 6t6 r~alisi avec succ~s. L’instrumentation en jauges de contrainte a confirm6 lea niveaux de...thermocouples, peintures thermosensibles et jauges de contraintes de fagon A mieux 6valuer leur comportement dans l’environnement du moteur. Aucune anomalie n’s
Failures of explaining away and screening off in described versus experienced causal learning scenarios.

PubMed

Rehder, Bob; Waldmann, Michael R

2017-02-01

Causal Bayes nets capture many aspects of causal thinking that set them apart from purely associative reasoning. However, some central properties of this normative theory routinely violated. In tasks requiring an understanding of explaining away and screening off, subjects often deviate from these principles and manifest the operation of an associative bias that we refer to as the rich-get-richer principle. This research focuses on these two failures comparing tasks in which causal scenarios are merely described (via verbal statements of the causal relations) versus experienced (via samples of data that manifest the intervariable correlations implied by the causal relations). Our key finding is that we obtained stronger deviations from normative predictions in the described conditions that highlight the instructed causal model compared to those that presented data. This counterintuitive finding indicate that a theory of causal reasoning and learning needs to integrate normative principles with biases people hold about causal relations.
Effects of Increased Psychiatric Treatment Contact and Acculturation on the Causal Beliefs of Chinese Immigrant Relatives of Individuals with Psychosis

PubMed Central

Lo, Graciete; Tu, Ming; Wu, Olivia; Anglin, Deidre; Saw, Anne; Chen, Fang-pei

2016-01-01

Encounters with Western psychiatric treatment and acculturation may influence causal beliefs of psychiatric illness endorsed by Chinese immigrant relatives, thus affecting help-seeking. We examined causal beliefs held by forty-six Chinese immigrant relatives and found that greater acculturation was associated with an increased number of causal beliefs. Further, as Western psychiatric treatment and acculturation increased, causal models expanded to incorporate biological/physical causes. However, frequency of Chinese immigrant relatives' endorsing spiritual beliefs did not appear to change with acculturation. Clinicians might thus account for spiritual beliefs in treatment even after acculturation increases and biological causal models proliferate. PMID:27127454
What Do Patients Think about the Cause of Their Mental Disorder? A Qualitative and Quantitative Analysis of Causal Beliefs of Mental Disorder in Inpatients in Psychosomatic Rehabilitation.

PubMed

Magaard, Julia Luise; Schulz, Holger; Brütt, Anna Levke

2017-01-01

Patients' causal beliefs about their mental disorders are important for treatment because they affect illness-related behaviours. However, there are few studies exploring patients' causal beliefs about their mental disorder. (a) To qualitatively explore patients' causal beliefs of their mental disorder, (b) to explore frequencies of patients stating causal beliefs, and (c) to investigate differences of causal beliefs according to patients' primary diagnoses. Inpatients in psychosomatic rehabilitation were asked an open-ended question about their three most important causal beliefs about their mental illness. Answers were obtained from 678 patients, with primary diagnoses of depression (N = 341), adjustment disorder (N = 75), reaction to severe stress (N = 57) and anxiety disorders (N = 40). Two researchers developed a category system inductively and categorised the reported causal beliefs. Qualitative analysis has been supplemented by logistic regression analyses. The causal beliefs were organized into twelve content-related categories. Causal beliefs referring to "problems at work" (47%) and "problems in social environment" (46%) were most frequently mentioned by patients with mental disorders. 35% of patients indicate causal beliefs related to "self/internal states". Patients with depression and patients with anxiety disorders stated similar causal beliefs, whereas patients with reactions to severe stress and adjustment disorders stated different causal beliefs in comparison to patients with depression. There was no opportunity for further exploration, because we analysed written documents. These results add a detailed insight to mentally ill patients' causal beliefs to illness perception literature. Additionally, evidence about differences in frequencies of causal beliefs between different illness groups complement previous findings. For future research it is important to clarify the relation between patients' causal beliefs and the chosen treatment.
What Do Patients Think about the Cause of Their Mental Disorder? A Qualitative and Quantitative Analysis of Causal Beliefs of Mental Disorder in Inpatients in Psychosomatic Rehabilitation

PubMed Central

Magaard, Julia Luise; Schulz, Holger; Brütt, Anna Levke

2017-01-01

Background Patients’ causal beliefs about their mental disorders are important for treatment because they affect illness-related behaviours. However, there are few studies exploring patients’ causal beliefs about their mental disorder. Objectives (a) To qualitatively explore patients’ causal beliefs of their mental disorder, (b) to explore frequencies of patients stating causal beliefs, and (c) to investigate differences of causal beliefs according to patients’ primary diagnoses. Method Inpatients in psychosomatic rehabilitation were asked an open-ended question about their three most important causal beliefs about their mental illness. Answers were obtained from 678 patients, with primary diagnoses of depression (N = 341), adjustment disorder (N = 75), reaction to severe stress (N = 57) and anxiety disorders (N = 40). Two researchers developed a category system inductively and categorised the reported causal beliefs. Qualitative analysis has been supplemented by logistic regression analyses. Results The causal beliefs were organized into twelve content-related categories. Causal beliefs referring to “problems at work” (47%) and “problems in social environment” (46%) were most frequently mentioned by patients with mental disorders. 35% of patients indicate causal beliefs related to “self/internal states”. Patients with depression and patients with anxiety disorders stated similar causal beliefs, whereas patients with reactions to severe stress and adjustment disorders stated different causal beliefs in comparison to patients with depression. Limitations There was no opportunity for further exploration, because we analysed written documents. Conclusions These results add a detailed insight to mentally ill patients’ causal beliefs to illness perception literature. Additionally, evidence about differences in frequencies of causal beliefs between different illness groups complement previous findings. For future research it is important to clarify the relation between patients’ causal beliefs and the chosen treatment. PMID:28056066
Electrophysiological difference between the representations of causal judgment and associative judgment in semantic memory.

PubMed

Chen, Qingfei; Liang, Xiuling; Lei, Yi; Li, Hong

2015-05-01

Causally related concepts like "virus" and "epidemic" and general associatively related concepts like "ring" and "emerald" are represented and accessed separately. The Evoked Response Potential (ERP) procedure was used to examine the representations of causal judgment and associative judgment in semantic memory. Participants were required to remember a task cue (causal or associative) presented at the beginning of each trial, and assess whether the relationship between subsequently presented words matched the initial task cue. The ERP data showed that an N400 effect (250-450 ms) was more negative for unrelated words than for all related words. Furthermore, the N400 effect elicited by causal relations was more positive than for associative relations in causal cue condition, whereas no significant difference was found in the associative cue condition. The centrally distributed late ERP component (650-750 ms) elicited by the causal cue condition was more positive than for the associative cue condition. These results suggested that the processing of causal judgment and associative judgment in semantic memory recruited different degrees of attentional and executive resources. Copyright © 2015 Elsevier B.V. All rights reserved.
Bayes and blickets: Effects of knowledge on causal induction in children and adults

PubMed Central

Griffiths, Thomas L.; Sobel, David M.; Tenenbaum, Joshua B.; Gopnik, Alison

2011-01-01

People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults’ judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children’s judgments (Experiments 3 and 5) agreed qualitatively with this account. PMID:21972897
Causal learning and inference as a rational process: the new synthesis.

PubMed

Holyoak, Keith J; Cheng, Patricia W

2011-01-01

Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with bayesian probabilistic inference. We describe this new synthesis, which views causal learning and inference as a fundamentally rational process, and review a sample of the empirical findings that support the causal framework over associative alternatives. Causal events, like all events in the distal world as opposed to our proximal perceptual input, are inherently unobservable. A central assumption of the causal approach is that humans (and potentially nonhuman animals) have been designed in such a way as to infer the most invariant causal relations for achieving their goals based on observed events. In contrast, the associative approach assumes that learners only acquire associations among important observed events, omitting the representation of the distal relations. By incorporating bayesian inference over distributions of causal strength and causal structures, along with noisy-logical (i.e., causal) functions for integrating the influences of multiple causes on a single effect, human judgments about causal strength and structure can be predicted accurately for relatively simple causal structures. Dynamic models of learning based on the causal framework can explain patterns of acquisition observed with serial presentation of contingency data and are consistent with available neuroimaging data. The approach has been extended to a diverse range of inductive tasks, including category-based and analogical inferences.
A quantum causal discovery algorithm

NASA Astrophysics Data System (ADS)

Giarmatzi, Christina; Costa, Fabio

2018-03-01

Finding a causal model for a set of classical variables is now a well-established task—but what about the quantum equivalent? Even the notion of a quantum causal model is controversial. Here, we present a causal discovery algorithm for quantum systems. The input to the algorithm is a process matrix describing correlations between quantum events. Its output consists of different levels of information about the underlying causal model. Our algorithm determines whether the process is causally ordered by grouping the events into causally ordered non-signaling sets. It detects if all relevant common causes are included in the process, which we label Markovian, or alternatively if some causal relations are mediated through some external memory. For a Markovian process, it outputs a causal model, namely the causal relations and the corresponding mechanisms, represented as quantum states and channels. Our algorithm opens the route to more general quantum causal discovery methods.
Time Functions as Utilities

NASA Astrophysics Data System (ADS)

Minguzzi, E.

2010-09-01

Every time function on spacetime gives a (continuous) total preordering of the spacetime events which respects the notion of causal precedence. The problem of the existence of a (semi-)time function on spacetime and the problem of recovering the causal structure starting from the set of time functions are studied. It is pointed out that these problems have an analog in the field of microeconomics known as utility theory. In a chronological spacetime the semi-time functions correspond to the utilities for the chronological relation, while in a K-causal (stably causal) spacetime the time functions correspond to the utilities for the K + relation (Seifert’s relation). By exploiting this analogy, we are able to import some mathematical results, most notably Peleg’s and Levin’s theorems, to the spacetime framework. As a consequence, we prove that a K-causal (i.e. stably causal) spacetime admits a time function and that the time or temporal functions can be used to recover the K + (or Seifert) relation which indeed turns out to be the intersection of the time or temporal orderings. This result tells us in which circumstances it is possible to recover the chronological or causal relation starting from the set of time or temporal functions allowed by the spacetime. Moreover, it is proved that a chronological spacetime in which the closure of the causal relation is transitive (for instance a reflective spacetime) admits a semi-time function. Along the way a new proof avoiding smoothing techniques is given that the existence of a time function implies stable causality, and a new short proof of the equivalence between K-causality and stable causality is given which takes advantage of Levin’s theorem and smoothing techniques.
Learning a theory of causality.

PubMed

Goodman, Noah D; Ullman, Tomer D; Tenenbaum, Joshua B

2011-01-01

The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be learned from co-occurrence of events. We begin by phrasing the causal Bayes nets theory of causality and a range of alternatives in a logical language for relational theories. This allows us to explore simultaneous inductive learning of an abstract theory of causality and a causal model for each of several causal systems. We find that the correct theory of causality can be learned relatively quickly, often becoming available before specific causal theories have been learned--an effect we term the blessing of abstraction. We then explore the effect of providing a variety of auxiliary evidence and find that a collection of simple perceptual input analyzers can help to bootstrap abstract knowledge. Together, these results suggest that the most efficient route to causal knowledge may be to build in not an abstract notion of causality but a powerful inductive learning mechanism and a variety of perceptual supports. While these results are purely computational, they have implications for cognitive development, which we explore in the conclusion.

Conjectures on the relations of linking and causality in causally simple spacetimes

NASA Astrophysics Data System (ADS)

Chernov, Vladimir

2018-05-01

We formulate the generalization of the Legendrian Low conjecture of Natario and Tod (proved by Nemirovski and myself before) to the case of causally simple spacetimes. We prove a weakened version of the corresponding statement. In all known examples, a causally simple spacetime can be conformally embedded as an open subset into some globally hyperbolic and the space of light rays in is an open submanifold of the space of light rays in . If this is always the case, this provides an approach to solving the conjectures relating causality and linking in causally simple spacetimes.
Quantum-coherent mixtures of causal relations

NASA Astrophysics Data System (ADS)

Maclean, Jean-Philippe W.; Ried, Katja; Spekkens, Robert W.; Resch, Kevin J.

2017-05-01

Understanding the causal influences that hold among parts of a system is critical both to explaining that system's natural behaviour and to controlling it through targeted interventions. In a quantum world, understanding causal relations is equally important, but the set of possibilities is far richer. The two basic ways in which a pair of time-ordered quantum systems may be causally related are by a cause-effect mechanism or by a common-cause acting on both. Here we show a coherent mixture of these two possibilities. We realize this nonclassical causal relation in a quantum optics experiment and derive a set of criteria for witnessing the coherence based on a quantum version of Berkson's effect, whereby two independent causes can become correlated on observation of their common effect. The interplay of causality and quantum theory lies at the heart of challenging foundational puzzles, including Bell's theorem and the search for quantum gravity.
Quantum-coherent mixtures of causal relations

PubMed Central

MacLean, Jean-Philippe W.; Ried, Katja; Spekkens, Robert W.; Resch, Kevin J.

2017-01-01

Understanding the causal influences that hold among parts of a system is critical both to explaining that system's natural behaviour and to controlling it through targeted interventions. In a quantum world, understanding causal relations is equally important, but the set of possibilities is far richer. The two basic ways in which a pair of time-ordered quantum systems may be causally related are by a cause-effect mechanism or by a common-cause acting on both. Here we show a coherent mixture of these two possibilities. We realize this nonclassical causal relation in a quantum optics experiment and derive a set of criteria for witnessing the coherence based on a quantum version of Berkson's effect, whereby two independent causes can become correlated on observation of their common effect. The interplay of causality and quantum theory lies at the heart of challenging foundational puzzles, including Bell's theorem and the search for quantum gravity. PMID:28485394
Quantum-coherent mixtures of causal relations.

PubMed

MacLean, Jean-Philippe W; Ried, Katja; Spekkens, Robert W; Resch, Kevin J

2017-05-09

Understanding the causal influences that hold among parts of a system is critical both to explaining that system's natural behaviour and to controlling it through targeted interventions. In a quantum world, understanding causal relations is equally important, but the set of possibilities is far richer. The two basic ways in which a pair of time-ordered quantum systems may be causally related are by a cause-effect mechanism or by a common-cause acting on both. Here we show a coherent mixture of these two possibilities. We realize this nonclassical causal relation in a quantum optics experiment and derive a set of criteria for witnessing the coherence based on a quantum version of Berkson's effect, whereby two independent causes can become correlated on observation of their common effect. The interplay of causality and quantum theory lies at the heart of challenging foundational puzzles, including Bell's theorem and the search for quantum gravity.
Causal Relations and Feature Similarity in Children's Inductive Reasoning

ERIC Educational Resources Information Center

Hayes, Brett K.; Thompson, Susan P.

2007-01-01

Four experiments examined the development of property induction on the basis of causal relations. In the first 2 studies, 5-year-olds, 8-year-olds, and adults were presented with triads in which a target instance was equally similar to 2 inductive bases but shared a causal antecedent feature with 1 of them. All 3 age groups used causal relations…
Deconstructing events: The neural bases for space, time, and causality

PubMed Central

Kranjec, Alexander; Cardillo, Eileen R.; Lehet, Matthew; Chatterjee, Anjan

2013-01-01

Space, time, and causality provide a natural structure for organizing our experience. These abstract categories allow us to think relationally in the most basic sense; understanding simple events require one to represent the spatial relations among objects, the relative durations of actions or movements, and links between causes and effects. The present fMRI study investigates the extent to which the brain distinguishes between these fundamental conceptual domains. Participants performed a one-back task with three conditions of interest (SPACE, TIME and CAUSALITY). Each condition required comparing relations between events in a simple verbal narrative. Depending on the condition, participants were instructed to either attend to the spatial, temporal, or causal characteristics of events, but between participants, each particular event relation appeared in all three conditions. Contrasts compared neural activity during each condition against the remaining two and revealed how thinking about events is deconstructed neurally. Space trials recruited neural areas traditionally associated with visuospatial processing, primarily bilateral frontal and occipitoparietal networks. Causality trials activated areas previously found to underlie causal thinking and thematic role assignment, such as left medial frontal, and left middle temporal gyri, respectively. Causality trials also produced activations in SMA, caudate, and cerebellum; cortical and subcortical regions associated with the perception of time at different timescales. The TIME contrast however, produced no significant effects. This pattern, indicating negative results for TIME trials, but positive effects for CAUSALITY trials in areas important for time perception, motivated additional overlap analyses to further probe relations between domains. The results of these analyses suggest a closer correspondence between time and causality than between time and space. PMID:21861674
Transitive closure of subsumption and causal relations in a large ontology of radiological diagnosis.

PubMed

Kahn, Charles E

2016-06-01

The Radiology Gamuts Ontology (RGO)-an ontology of diseases, interventions, and imaging findings-was developed to aid in decision support, education, and translational research in diagnostic radiology. The ontology defines a subsumption (is_a) relation between more general and more specific terms, and a causal relation (may_cause) to express the relationship between disorders and their possible imaging manifestations. RGO incorporated 19,745 terms with their synonyms and abbreviations, 1768 subsumption relations, and 55,558 causal relations. Transitive closure was computed iteratively; it yielded 2154 relations over subsumption and 1,594,896 relations over causality. Five causal cycles were discovered, all with path length of no more than 5. The graph-theoretic metrics of in-degree and out-degree were explored; the most useful metric to prioritize modification of the ontology was found to be the product of the in-degree of transitive closure over subsumption and the out-degree of transitive closure over causality. Two general types of error were identified: (1) causal assertions that used overly general terms because they implicitly assumed an organ-specific context and (2) subsumption relations where a site-specific disorder was asserted to be a subclass of the general disorder. Transitive closure helped identify incorrect assertions, prioritized and guided ontology revision, and aided resources that applied the ontology's knowledge. Copyright © 2016 Elsevier Inc. All rights reserved.
Causality Analysis of Neural Connectivity: Critical Examination of Existing Methods and Advances of New Methods

PubMed Central

Hu, Sanqing; Dai, Guojun; Worrell, Gregory A.; Dai, Qionghai; Liang, Hualou

2012-01-01

Granger causality (GC) is one of the most popular measures to reveal causality influence of time series and has been widely applied in economics and neuroscience. Especially, its counterpart in frequency domain, spectral GC, as well as other Granger-like causality measures have recently been applied to study causal interactions between brain areas in different frequency ranges during cognitive and perceptual tasks. In this paper, we show that: 1) GC in time domain cannot correctly determine how strongly one time series influences the other when there is directional causality between two time series, and 2) spectral GC and other Granger-like causality measures have inherent shortcomings and/or limitations because of the use of the transfer function (or its inverse matrix) and partial information of the linear regression model. On the other hand, we propose two novel causality measures (in time and frequency domains) for the linear regression model, called new causality and new spectral causality, respectively, which are more reasonable and understandable than GC or Granger-like measures. Especially, from one simple example, we point out that, in time domain, both new causality and GC adopt the concept of proportion, but they are defined on two different equations where one equation (for GC) is only part of the other (for new causality), thus the new causality is a natural extension of GC and has a sound conceptual/theoretical basis, and GC is not the desired causal influence at all. By several examples, we confirm that new causality measures have distinct advantages over GC or Granger-like measures. Finally, we conduct event-related potential causality analysis for a subject with intracranial depth electrodes undergoing evaluation for epilepsy surgery, and show that, in the frequency domain, all measures reveal significant directional event-related causality, but the result from new spectral causality is consistent with event-related time–frequency power spectrum activity. The spectral GC as well as other Granger-like measures are shown to generate misleading results. The proposed new causality measures may have wide potential applications in economics and neuroscience. PMID:21511564
Unveiling causal activity of complex networks

NASA Astrophysics Data System (ADS)

Williams-García, Rashid V.; Beggs, John M.; Ortiz, Gerardo

2017-07-01

We introduce a novel tool for analyzing complex network dynamics, allowing for cascades of causally-related events, which we call causal webs (c-webs), to be separated from other non-causally-related events. This tool shows that traditionally-conceived avalanches may contain mixtures of spatially-distinct but temporally-overlapping cascades of events, and dynamical disorder or noise. In contrast, c-webs separate these components, unveiling previously hidden features of the network and dynamics. We apply our method to mouse cortical data with resulting statistics which demonstrate for the first time that neuronal avalanches are not merely composed of causally-related events. The original version of this article was uploaded to the arXiv on March 17th, 2016 [1].
Making sense of (exceptional) causal relations. A cross-cultural and cross-linguistic study.

PubMed

Le Guen, Olivier; Samland, Jana; Friedrich, Thomas; Hanus, Daniel; Brown, Penelope

2015-01-01

In order to make sense of the world, humans tend to see causation almost everywhere. Although most causal relations may seem straightforward, they are not always construed in the same way cross-culturally. In this study, we investigate concepts of "chance," "coincidence," or "randomness" that refer to assumed relations between intention, action, and outcome in situations, and we ask how people from different cultures make sense of such non-law-like connections. Based on a framework proposed by Alicke (2000), we administered a task that aims to be a neutral tool for investigating causal construals cross-culturally and cross-linguistically. Members of four different cultural groups, rural Mayan Yucatec and Tseltal speakers from Mexico and urban students from Mexico and Germany, were presented with a set of scenarios involving various types of causal and non-causal relations and were asked to explain the described events. Three links varied as to whether they were present or not in the scenarios: Intention-to-Action, Action-to-Outcome, and Intention-to-Outcome. Our results show that causality is recognized in all four cultural groups. However, how causality and especially non-law-like relations are interpreted depends on the type of links, the cultural background and the language used. In all three groups, Action-to-Outcome is the decisive link for recognizing causality. Despite the fact that the two Mayan groups share similar cultural backgrounds, they display different ideologies regarding concepts of non-law-like relations. The data suggests that the concept of "chance" is not universal, but seems to be an explanation that only some cultural groups draw on to make sense of specific situations. Of particular importance is the existence of linguistic concepts in each language that trigger ideas of causality in the responses from each cultural group.
What is the nature of causality in the brain? - Inherently probabilistic. Comment on "Foundational perspectives on causality in large-scale brain networks" by M. Mannino and S.L. Bressler

NASA Astrophysics Data System (ADS)

Dhamala, Mukesh

2015-12-01

Understanding cause-and-effect (causal) relations from observations concerns all sciences including neuroscience. Appropriately defining causality and its nature, though, has been a topic of active discussion for philosophers and scientists for centuries. Although brain research, particularly functional neuroimaging research, is now moving rapidly beyond identification of brain regional activations towards uncovering causal relations between regions, the nature of causality has not be been thoroughly described and resolved. In the current review article [1], Mannino and Bressler take us on a beautiful journey into the history of the work on causality and make a well-reasoned argument that the causality in the brain is inherently probabilistic. This notion is consistent with brain anatomy and functions, and is also inclusive of deterministic cases of inputs leading to outputs in the brain.
A Theory of Causal Learning in Children: Causal Maps and Bayes Nets

ERIC Educational Resources Information Center

Gopnik, Alison; Glymour, Clark; Sobel, David M.; Schulz, Laura E.; Kushnir, Tamar; Danks, David

2004-01-01

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously…
Earthquake prediction: the interaction of public policy and science.

PubMed Central

Jones, L M

1996-01-01

Earthquake prediction research has searched for both informational phenomena, those that provide information about earthquake hazards useful to the public, and causal phenomena, causally related to the physical processes governing failure on a fault, to improve our understanding of those processes. Neither informational nor causal phenomena are a subset of the other. I propose a classification of potential earthquake predictors of informational, causal, and predictive phenomena, where predictors are causal phenomena that provide more accurate assessments of the earthquake hazard than can be gotten from assuming a random distribution. Achieving higher, more accurate probabilities than a random distribution requires much more information about the precursor than just that it is causally related to the earthquake. PMID:11607656
The Development of Causal Categorization

ERIC Educational Resources Information Center

Hayes, Brett K.; Rehder, Bob

2012-01-01

Two experiments examined the impact of causal relations between features on categorization in 5- to 6-year-old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs.…
Is there a relation between the 2D Causal Set action and the Lorentzian Gauss-Bonnet theorem?

NASA Astrophysics Data System (ADS)

Benincasa, Dionigi M. T.

2011-07-01

We investigate the relation between the two dimensional Causal Set action, Script S, and the Lorentzian Gauss-Bonnet theorem (LGBT). We give compelling reasons why the answer to the title's question is no. In support of this point of view we calculate the causal set inspired action of causal intervals in some two dimensional spacetimes: Minkowski, the flat cylinder and the flat trousers.
When Work is Related to Disease, What Establishes Evidence for a Causal Relation?

PubMed

Verbeek, Jos

2012-06-01

Establishing a causal relationship between factors at work and disease is difficult for occupational physicians and researchers. This paper seeks to provide arguments for the judgement of evidence of causality in observational studies that relate work factors to disease. I derived criteria for the judgement of evidence of causality from the following sources: the criteria list of Hill, the approach by Rothman, the methods used by International Agency for Research on Cancer (IARC), and methods used by epidemiologists. The criteria are applied to two cases of putative occupational diseases; breast cancer caused by shift work and aerotoxic syndrome. Only three of the Hill criteria can be applied to an actual study. Rothman stresses the importance of confounding and alternative explanations than the putative cause. IARC closely follows Hill, but they also incorporate other than epidemiological evidence. Applied to shift work and breast cancer, these results have found moderate evidence for a causal relationship, but applied to the aerotoxic syndrome, there is an absence of evidence of causality. There are no ready to use algorithms for judgement of evidence of causality. Criteria from different sources lead to similar results and can make a conclusion of causality more or less likely.
When Work is Related to Disease, What Establishes Evidence for a Causal Relation?

PubMed Central

2012-01-01

Establishing a causal relationship between factors at work and disease is difficult for occupational physicians and researchers. This paper seeks to provide arguments for the judgement of evidence of causality in observational studies that relate work factors to disease. I derived criteria for the judgement of evidence of causality from the following sources: the criteria list of Hill, the approach by Rothman, the methods used by International Agency for Research on Cancer (IARC), and methods used by epidemiologists. The criteria are applied to two cases of putative occupational diseases; breast cancer caused by shift work and aerotoxic syndrome. Only three of the Hill criteria can be applied to an actual study. Rothman stresses the importance of confounding and alternative explanations than the putative cause. IARC closely follows Hill, but they also incorporate other than epidemiological evidence. Applied to shift work and breast cancer, these results have found moderate evidence for a causal relationship, but applied to the aerotoxic syndrome, there is an absence of evidence of causality. There are no ready to use algorithms for judgement of evidence of causality. Criteria from different sources lead to similar results and can make a conclusion of causality more or less likely. PMID:22993715
Non-Bayesian Inference: Causal Structure Trumps Correlation

ERIC Educational Resources Information Center

Bes, Benedicte; Sloman, Steven; Lucas, Christopher G.; Raufaste, Eric

2012-01-01

The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more…
Causal Relations Drive Young Children's Induction, Naming, and Categorization

ERIC Educational Resources Information Center

Opfer, John E.; Bulloch, Megan J.

2007-01-01

A number of recent models and experiments have suggested that evidence of early category-based induction is an artifact of perceptual cues provided by experimenters. We tested these accounts against the prediction that different relations (causal versus non-causal) determine the types of perceptual similarity by which children generalize. Young…
Behavioral Variability, Learning Processes, and Creativity

DTIC Science & Technology

1992-03-01

socio- logiques, 6conomiques ou iddologiques auxquels n𔄀chappent pas les entreprises dducatives. Nous retrouvons IA trois themes de d~bat assez courants...pour ne pas dire traditionnels, dans les milieux pidagogiques, trois arguments que I’on pourrait reforinuler de la maniere suivante: 1 . Les...vain de concevoir I’enseignement comme un domaine d’ap- plication de la psychologie de I’apprentissage. Sans doute aucune de ces trois propositions ne

Making sense of (exceptional) causal relations. A cross-cultural and cross-linguistic study

PubMed Central

Le Guen, Olivier; Samland, Jana; Friedrich, Thomas; Hanus, Daniel; Brown, Penelope

2015-01-01

In order to make sense of the world, humans tend to see causation almost everywhere. Although most causal relations may seem straightforward, they are not always construed in the same way cross-culturally. In this study, we investigate concepts of “chance,” “coincidence,” or “randomness” that refer to assumed relations between intention, action, and outcome in situations, and we ask how people from different cultures make sense of such non-law-like connections. Based on a framework proposed by Alicke (2000), we administered a task that aims to be a neutral tool for investigating causal construals cross-culturally and cross-linguistically. Members of four different cultural groups, rural Mayan Yucatec and Tseltal speakers from Mexico and urban students from Mexico and Germany, were presented with a set of scenarios involving various types of causal and non-causal relations and were asked to explain the described events. Three links varied as to whether they were present or not in the scenarios: Intention-to-Action, Action-to-Outcome, and Intention-to-Outcome. Our results show that causality is recognized in all four cultural groups. However, how causality and especially non-law-like relations are interpreted depends on the type of links, the cultural background and the language used. In all three groups, Action-to-Outcome is the decisive link for recognizing causality. Despite the fact that the two Mayan groups share similar cultural backgrounds, they display different ideologies regarding concepts of non-law-like relations. The data suggests that the concept of “chance” is not universal, but seems to be an explanation that only some cultural groups draw on to make sense of specific situations. Of particular importance is the existence of linguistic concepts in each language that trigger ideas of causality in the responses from each cultural group. PMID:26579028
Reasoning with Causal Cycles

ERIC Educational Resources Information Center

Rehder, Bob

2017-01-01

This article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models (CGMs) have enjoyed success modeling category-based judgments as well as a number of other cognitive phenomena, CGMs are only able to represent causal structures that are acyclic. A number of new…
Preschool Children Learn about Causal Structure from Conditional Interventions

ERIC Educational Resources Information Center

Schulz, Laura E.; Gopnik, Alison; Glymour, Clark

2007-01-01

The conditional intervention principle is a formal principle that relates patterns of interventions and outcomes to causal structure. It is a central assumption of experimental design and the causal Bayes net formalism. Two studies suggest that preschoolers can use the conditional intervention principle to distinguish causal chains, common cause…
Is There a Causal Relation between Mathematical Creativity and Mathematical Problem-Solving Performance?

ERIC Educational Resources Information Center

Tyagi, Tarun Kumar

2016-01-01

The relationship between mathematical creativity (MC) and mathematical problem-solving performance (MP) has often been studied but the causal relation between these two constructs has yet to be clearly reported. The main purpose of this study was to define the causal relationship between MC and MP. Data from a representative sample of 480…
Causal beliefs about depression in different cultural groups—what do cognitive psychological theories of causal learning and reasoning predict?

PubMed Central

Hagmayer, York; Engelmann, Neele

2014-01-01

Cognitive psychological research focuses on causal learning and reasoning while cognitive anthropological and social science research tend to focus on systems of beliefs. Our aim was to explore how these two types of research can inform each other. Cognitive psychological theories (causal model theory and causal Bayes nets) were used to derive predictions for systems of causal beliefs. These predictions were then applied to lay theories of depression as a specific test case. A systematic literature review on causal beliefs about depression was conducted, including original, quantitative research. Thirty-six studies investigating 13 non-Western and 32 Western cultural groups were analyzed by classifying assumed causes and preferred forms of treatment into common categories. Relations between beliefs and treatment preferences were assessed. Substantial agreement between cultural groups was found with respect to the impact of observable causes. Stress was generally rated as most important. Less agreement resulted for hidden, especially supernatural causes. Causal beliefs were clearly related to treatment preferences in Western groups, while evidence was mostly lacking for non-Western groups. Overall predictions were supported, but there were considerable methodological limitations. Pointers to future research, which may combine studies on causal beliefs with experimental paradigms on causal reasoning, are given. PMID:25505432
Causal Superlearning Arising from Interactions Among Cues

PubMed Central

Urushihara, Kouji; Miller, Ralph R.

2017-01-01

Superconditioning refers to supernormal responding to a conditioned stimulus (CS) that sometimes occurs in classical conditioning when the CS is paired with an unconditioned stimulus (US) in the presence of a conditioned inhibitor for that US. In the present research, we conducted four experiments to investigate causal superlearning, a phenomenon in human causal learning analogous to superconditioning. Experiment 1 demonstrated superlearning relative to appropriate control conditions. Experiment 2 showed that superlearning wanes when the number of cues used in an experiment is relatively large. Experiment 3 determined that even when relatively many cues are used, superlearning can be observed provided testing is conducted immediately after training, which is problematic for explanations by most contemporary learning theories. Experiment 4 found that ratings of a superlearning cue are weaker than those to the training excitor which gives basis to the conditioned inhibitor-like causal preventor used during causal superlearning training. This is inconsistent with the prediction by propositional reasoning accounts of causal cue competition, but is readily explained by associative learning models. In sum, the current experiments revealed some weaknesses of both the associative and propositional reasoning models with respect to causal superlearning. PMID:28383940
Causal and causally separable processes

NASA Astrophysics Data System (ADS)

Oreshkov, Ognyan; Giarmatzi, Christina

2016-09-01

The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A and B, either A is in the causal past of B, B is in the causal past of A, or A and B are space-like separated. Operationally, the meaning of these order relations corresponds to constraints on the possible correlations between experiments performed in the vicinities of the respective events: if A is in the causal past of B, an experimenter at A could signal to an experimenter at B but not the other way around, while if A and B are space-like separated, no signaling is possible in either direction. In the context of a concrete physical theory, the correlations compatible with a given causal configuration may obey further constraints. For instance, space-like correlations in quantum mechanics arise from local measurements on joint quantum states, while time-like correlations are established via quantum channels. Similarly to other variables, however, the causal order of a set of events could be random, and little is understood about the constraints that causality implies in this case. A main difficulty concerns the fact that the order of events can now generally depend on the operations performed at the locations of these events, since, for instance, an operation at A could influence the order in which B and C occur in A’s future. So far, no formal theory of causality compatible with such dynamical causal order has been developed. Apart from being of fundamental interest in the context of inferring causal relations, such a theory is imperative for understanding recent suggestions that the causal order of events in quantum mechanics can be indefinite. Here, we develop such a theory in the general multipartite case. Starting from a background-independent definition of causality, we derive an iteratively formulated canonical decomposition of multipartite causal correlations. For a fixed number of settings and outcomes for each party, these correlations form a polytope whose facets define causal inequalities. The case of quantum correlations in this paradigm is captured by the process matrix formalism. We investigate the link between causality and the closely related notion of causal separability of quantum processes, which we here define rigorously in analogy with the link between Bell locality and separability of quantum states. We show that causality and causal separability are not equivalent in general by giving an example of a physically admissible tripartite quantum process that is causal but not causally separable. We also show that there are causally separable quantum processes that become non-causal if extended by supplying the parties with entangled ancillas. This motivates the concepts of extensibly causal and extensibly causally separable (ECS) processes, for which the respective property remains invariant under extension. We characterize the class of ECS quantum processes in the tripartite case via simple conditions on the form of the process matrix. We show that the processes realizable by classically controlled quantum circuits are ECS and conjecture that the reverse also holds.
Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment.

PubMed

Johnson, Samuel G B; Ahn, Woo-kyoung

2015-09-01

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory (e.g., exercising, becoming thirsty, drinking water) led to transitive causal judgments. On the other hand, chains schematized as multiple chunks (e.g., having sex, becoming pregnant, becoming nauseous) led to intransitive judgments despite strong intermediate links ((Experiments 1-3). Normative accounts of causal intransitivity could not explain these intransitive judgments (Experiments 4 and 5). Copyright © 2015 Cognitive Science Society, Inc.
Perceived Causal Relations: Novel Methodology for Assessing Client Attributions about Causal Associations between Variables Including Symptoms and Functional Impairment

ERIC Educational Resources Information Center

Frewen, Paul A.; Allen, Samantha L.; Lanius, Ruth A.; Neufeld, Richard W. J.

2012-01-01

Researchers have argued that the investigation of causal interrelationships between symptoms may help explain the high comorbidity rate between certain psychiatric disorders. Clients' own attributions concerning the causal interrelationships linking the co-occurrence of their symptoms represent data that may inform their clinical case…
The Specification of Causal Models with Tetrad IV: A Review

ERIC Educational Resources Information Center

Landsheer, J. A.

2010-01-01

Tetrad IV is a program designed for the specification of causal models. It is specifically designed to search for causal relations, but also offers the possibility to estimate the parameters of a structural equation model. It offers a remarkable graphical user interface, which facilitates building, evaluating, and searching for causal models. The…
Learning to make things happen: Infants' observational learning of social and physical causal events.

PubMed

Waismeyer, Anna; Meltzoff, Andrew N

2017-10-01

Infants learn about cause and effect through hands-on experience; however, they also can learn about causality simply from observation. Such observational causal learning is a central mechanism by which infants learn from and about other people. Across three experiments, we tested infants' observational causal learning of both social and physical causal events. Experiment 1 assessed infants' learning of a physical event in the absence of visible spatial contact between the causes and effects. Experiment 2 developed a novel paradigm to assess whether infants could learn about a social causal event from third-party observation of a social interaction between two people. Experiment 3 compared learning of physical and social events when the outcomes occurred probabilistically (happening some, but not all, of the time). Infants demonstrated significant learning in all three experiments, although learning about probabilistic cause-effect relations was most difficult. These findings about infant observational causal learning have implications for children's rapid nonverbal learning about people, things, and their causal relations. Copyright © 2017 Elsevier Inc. All rights reserved.
A Kernel Embedding-Based Approach for Nonstationary Causal Model Inference.

PubMed

Hu, Shoubo; Chen, Zhitang; Chan, Laiwan

2018-05-01

Although nonstationary data are more common in the real world, most existing causal discovery methods do not take nonstationarity into consideration. In this letter, we propose a kernel embedding-based approach, ENCI, for nonstationary causal model inference where data are collected from multiple domains with varying distributions. In ENCI, we transform the complicated relation of a cause-effect pair into a linear model of variables of which observations correspond to the kernel embeddings of the cause-and-effect distributions in different domains. In this way, we are able to estimate the causal direction by exploiting the causal asymmetry of the transformed linear model. Furthermore, we extend ENCI to causal graph discovery for multiple variables by transforming the relations among them into a linear nongaussian acyclic model. We show that by exploiting the nonstationarity of distributions, both cause-effect pairs and two kinds of causal graphs are identifiable under mild conditions. Experiments on synthetic and real-world data are conducted to justify the efficacy of ENCI over major existing methods.
A review of current evidence for the causal impact of attentional bias on fear and anxiety.

PubMed

Van Bockstaele, Bram; Verschuere, Bruno; Tibboel, Helen; De Houwer, Jan; Crombez, Geert; Koster, Ernst H W

2014-05-01

Prominent cognitive theories postulate that an attentional bias toward threatening information contributes to the etiology, maintenance, or exacerbation of fear and anxiety. In this review, we investigate to what extent these causal claims are supported by sound empirical evidence. Although differences in attentional bias are associated with differences in fear and anxiety, this association does not emerge consistently. Moreover, there is only limited evidence that individual differences in attentional bias are related to individual differences in fear or anxiety. In line with a causal relation, some studies show that attentional bias precedes fear or anxiety in time. However, other studies show that fear and anxiety can precede the onset of attentional bias, suggesting circular or reciprocal causality. Importantly, a recent line of experimental research shows that changes in attentional bias can lead to changes in anxiety. Yet changes in fear and anxiety also lead to changes in attentional bias, which confirms that the relation between attentional bias and fear and anxiety is unlikely to be unidirectional. Finally, a similar causal relation between interpretation bias and anxiety has been documented. In sum, there is evidence in favor of causality, yet a strict unidirectional cause-effect model is unlikely to hold. The relation between attentional bias and fear and anxiety is best described as a bidirectional, maintaining, or mutually reinforcing relation.
Evolution of Religious Beliefs

ScienceCinema

None

2018-05-11

Humans may be distinguished from all other animals in having beliefs about the causal interaction of physical objects. Causal beliefs are a developmental primitive in human children; animals, by contrast, have very few causal beliefs. The origin of human causal beliefs comes from the evolutionary advantage it gave in relation to complex tool making and use. Causal beliefs gave rise religion and mystical thinking as our ancestors wanted to know the causes of events that affected their lives.
Evolution of Religious Beliefs

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

Humans may be distinguished from all other animals in having beliefs about the causal interaction of physical objects. Causal beliefs are a developmental primitive in human children; animals, by contrast, have very few causal beliefs. The origin of human causal beliefs comes from the evolutionary advantage it gave in relation to complex tool making and use. Causal beliefs gave rise religion and mystical thinking as our ancestors wanted to know the causes of events that affected their lives.
Replicating the benefits of Deutschian closed timelike curves without breaking causality

NASA Astrophysics Data System (ADS)

Yuan, Xiao; Assad, Syed M.; Thompson, Jayne; Haw, Jing Yan; Vedral, Vlatko; Ralph, Timothy C.; Lam, Ping Koy; Weedbrook, Christian; Gu, Mile

2015-11-01

In general relativity, closed timelike curves can break causality with remarkable and unsettling consequences. At the classical level, they induce causal paradoxes disturbing enough to motivate conjectures that explicitly prevent their existence. At the quantum level such problems can be resolved through the Deutschian formalism, however this induces radical benefits—from cloning unknown quantum states to solving problems intractable to quantum computers. Instinctively, one expects these benefits to vanish if causality is respected. Here we show that in harnessing entanglement, we can efficiently solve NP-complete problems and clone arbitrary quantum states—even when all time-travelling systems are completely isolated from the past. Thus, the many defining benefits of Deutschian closed timelike curves can still be harnessed, even when causality is preserved. Our results unveil a subtle interplay between entanglement and general relativity, and significantly improve the potential of probing the radical effects that may exist at the interface between relativity and quantum theory.
'Mendelian randomization': an approach for exploring causal relations in epidemiology.

PubMed

Gupta, V; Walia, G K; Sachdeva, M P

2017-04-01

To assess the current status of Mendelian randomization (MR) approach in effectively influencing the observational epidemiology for examining causal relationships. Narrative review on studies related to principle, strengths, limitations, and achievements of MR approach. Observational epidemiological studies have repeatedly produced several beneficiary associations which were discarded when tested by standard randomized controlled trials (RCTs). The technique which is more feasible, highly similar to RCTs, and has the potential to establish a causal relationship between modifiable exposures and disease outcomes is known as MR. The technique uses genetic variants related to modifiable traits/exposures as instruments for detecting causal and directional associations with outcomes. In the last decade, the approach of MR has methodologically developed and progressed to a stage of high acceptance among the epidemiologists and is gradually expanding the landscape of causal relationships in non-communicable chronic diseases. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
Experimental verification of an indefinite causal order

PubMed Central

Rubino, Giulia; Rozema, Lee A.; Feix, Adrien; Araújo, Mateus; Zeuner, Jonas M.; Procopio, Lorenzo M.; Brukner, Časlav; Walther, Philip

2017-01-01

Investigating the role of causal order in quantum mechanics has recently revealed that the causal relations of events may not be a priori well defined in quantum theory. Although this has triggered a growing interest on the theoretical side, creating processes without a causal order is an experimental task. We report the first decisive demonstration of a process with an indefinite causal order. To do this, we quantify how incompatible our setup is with a definite causal order by measuring a “causal witness.” This mathematical object incorporates a series of measurements that are designed to yield a certain outcome only if the process under examination is not consistent with any well-defined causal order. In our experiment, we perform a measurement in a superposition of causal orders—without destroying the coherence—to acquire information both inside and outside of a “causally nonordered process.” Using this information, we experimentally determine a causal witness, demonstrating by almost 7 SDs that the experimentally implemented process does not have a definite causal order. PMID:28378018
The development of causal reasoning.

PubMed

Kuhn, Deanna

2012-05-01

How do inference rules for causal learning themselves change developmentally? A model of the development of causal reasoning must address this question, as well as specify the inference rules. Here, the evidence for developmental changes in processes of causal reasoning is reviewed, with the distinction made between diagnostic causal inference and causal prediction. Also addressed is the paradox of a causal reasoning literature that highlights the competencies of young children and the proneness to error among adults. WIREs Cogn Sci 2012, 3:327-335. doi: 10.1002/wcs.1160 For further resources related to this article, please visit the WIREs website. Copyright © 2012 John Wiley & Sons, Ltd.
Property Transmission: An Explanatory Account of the Role of Similarity Information in Causal Inference

ERIC Educational Resources Information Center

White, Peter A.

2009-01-01

Many kinds of common and easily observed causal relations exhibit property transmission, which is a tendency for the causal object to impose its own properties on the effect object. It is proposed that property transmission becomes a general and readily available hypothesis used to make interpretations and judgments about causal questions under…

How to Be Causal: Time, Spacetime and Spectra

ERIC Educational Resources Information Center

Kinsler, Paul

2011-01-01

I explain a simple definition of causality in widespread use, and indicate how it links to the Kramers-Kronig relations. The specification of causality in terms of temporal differential equations then shows us the way to write down dynamical models so that their causal nature "in the sense used here" should be obvious to all. To extend existing…
A Complex Systems Approach to Causal Discovery in Psychiatry.

PubMed

Saxe, Glenn N; Statnikov, Alexander; Fenyo, David; Ren, Jiwen; Li, Zhiguo; Prasad, Meera; Wall, Dennis; Bergman, Nora; Briggs, Ernestine C; Aliferis, Constantin

2016-01-01

Conventional research methodologies and data analytic approaches in psychiatric research are unable to reliably infer causal relations without experimental designs, or to make inferences about the functional properties of the complex systems in which psychiatric disorders are embedded. This article describes a series of studies to validate a novel hybrid computational approach--the Complex Systems-Causal Network (CS-CN) method-designed to integrate causal discovery within a complex systems framework for psychiatric research. The CS-CN method was first applied to an existing dataset on psychopathology in 163 children hospitalized with injuries (validation study). Next, it was applied to a much larger dataset of traumatized children (replication study). Finally, the CS-CN method was applied in a controlled experiment using a 'gold standard' dataset for causal discovery and compared with other methods for accurately detecting causal variables (resimulation controlled experiment). The CS-CN method successfully detected a causal network of 111 variables and 167 bivariate relations in the initial validation study. This causal network had well-defined adaptive properties and a set of variables was found that disproportionally contributed to these properties. Modeling the removal of these variables resulted in significant loss of adaptive properties. The CS-CN method was successfully applied in the replication study and performed better than traditional statistical methods, and similarly to state-of-the-art causal discovery algorithms in the causal detection experiment. The CS-CN method was validated, replicated, and yielded both novel and previously validated findings related to risk factors and potential treatments of psychiatric disorders. The novel approach yields both fine-grain (micro) and high-level (macro) insights and thus represents a promising approach for complex systems-oriented research in psychiatry.
[A study of relation between hopelessness and causal attribution in school-aged children].

PubMed

Sakurai, S

1989-12-01

This study was conducted to investigate the relation between hopelessness and causal attribution in Japanese school-aged children. In Study 1, the Japanese edition of hopelessness scale for children developed by Kazdin, French, Unis, Esveldt-Dawsan, and Sherick (1983) was constructed. Seventeen original items were translated into Japanese and they were administrated to 405 fifth- and sixth-graders. All of the items could be included to the Japanese edition of hopelessness scale. The reliability and validity was examined. In Study 2, the relation between hopelessness and causal attribution in children were investigated. The causal attribution questionnaire developed by Higuchi, Kambare, and Otsuka (1983) and the hopelessness scale developed by Study 1 were administered to 188 sixth-graders. Children with high scores in hopelessness scale significantly attributed negative events to much more effort factor than children with low scores. It supports neither the reformulated learned helplessness model nor the causal attribution theory of achievement motivation. It was explained mainly from points of self-serving attribution, cultural difference, and social desirability. Some questions were discussed for developing studies on depression and causal attribution in Japan.
Perceived causal relations between anxiety, posttraumatic stress and depression: extension to moderation, mediation, and network analysis.

PubMed

Frewen, Paul A; Schmittmann, Verena D; Bringmann, Laura F; Borsboom, Denny

2013-01-01

Previous research demonstrates that posttraumatic memory reexperiencing, depression, anxiety, and guilt-shame are frequently co-occurring problems that may be causally related. The present study utilized Perceived Causal Relations (PCR) scaling in order to assess participants' own attributions concerning whether and to what degree these co-occurring problems may be causally interrelated. 288 young adults rated the frequency and respective PCR scores associating their symptoms of posttraumatic reexperiencing, depression, anxiety, and guilt-shame. PCR scores were found to moderate associations between the frequency of posttraumatic memory reexperiencing, depression, anxiety, and guilt-shame. Network analyses showed that the number of feedback loops between PCR scores was positively associated with symptom frequencies. Results tentatively support the interpretation of PCR scores as moderators of the association between different psychological problems, and lend support to the hypothesis that increased symptom frequencies are observed in the presence of an increased number of causal feedback loops between symptoms. Additionally, a perceived causal role for the reexperiencing of traumatic memories in exacerbating emotional disturbance was identified.
Relativistic causality

NASA Astrophysics Data System (ADS)

Valente, Giovanni; Owen Weatherall, James

2014-11-01

Relativity theory is often taken to include, or to imply, a prohibition on superluminal propagation of causal processes. Yet, what exactly the prohibition on superluminal propagation amounts to and how one should deal with its possible violation have remained open philosophical problems, both in the context of the metaphysics of causation and the foundations of physics. In particular, recent work in philosophy of physics has focused on the causal structure of spacetime in relativity theory and on how this causal structure manifests itself in our most fundamental theories of matter. These topics were the subject of a workshop on "Relativistic Causality in Quantum Field Theory and General Relativity" that we organized (along with John Earman) at the Center for Philosophy of Science in Pittsburgh on April 5-7, 2013. The present Special Issue comprises contributions by speakers in that workshop as well as several other experts exploring different aspects of relativistic causality. We are grateful to the journal for hosting this Special Issue, to the journal's managing editor, Femke Kuiling, for her help and support in putting the issue together, and to the authors and the referees for their excellent work.
Effects of causality on the fluidity and viscous horizon of quark-gluon plasma

NASA Astrophysics Data System (ADS)

Rahaman, Mahfuzur; Alam, Jan-e.

2018-05-01

The second-order Israel-Stewart-M u ̈ller relativistic hydrodynamics was applied to study the effects of causality on the acoustic oscillation in relativistic fluid. Causal dispersion relations have been derived with nonvanishing shear viscosity, bulk viscosity, and thermal conductivity at nonzero temperature and baryonic chemical potential. These relations have been used to investigate the fluidity of quark-gluon plasma (QGP) at finite temperature (T ). Results of the first-order dissipative hydrodynamics have been obtained as a limiting case of the second-order theory. The effects of the causality on the fluidity near the transition point and on the viscous horizon are found to be significant. We observe that the inclusion of causality increases the value of fluidity measure of QGP near Tc and hence makes the flow strenuous. It was also shown that the inclusion of the large magnetic field in the causal hydrodynamics alters the fluidity of QGP.
The Relative Predictive Contribution and Causal Role of Phoneme Awareness, Rhyme Awareness, and Verbal Short-Term Memory in Reading Skills: A Review

ERIC Educational Resources Information Center

Melby-Lervag, Monica

2012-01-01

The acknowledgement that educational achievement is highly dependent on successful reading development has led to extensive research on its underlying factors. A strong argument has been made for a causal relationship between reading and phoneme awareness; similarly, causal relations have been suggested for reading with short-term memory and rhyme…
Genetic causal beliefs about obesity, self-efficacy for weight control, and obesity-related behaviours in a middle-aged female cohort.

PubMed

Knerr, Sarah; Bowen, Deborah J; Beresford, Shirley A A; Wang, Catharine

2016-01-01

Obesity is a heritable condition with well-established risk-reducing behaviours. Studies have shown that beliefs about the causes of obesity are associated with diet and exercise behaviour. Identifying mechanisms linking causal beliefs and behaviours is important for obesity prevention and control. Cross-sectional multi-level regression analyses of self-efficacy for weight control as a possible mediator of obesity attributions (diet, physical activity, genetic) and preventive behaviours in 487 non-Hispanic White women from South King County, Washington. Self-reported daily fruit and vegetable intake and weekly leisure-time physical activity. Diet causal beliefs were positively associated with fruit and vegetable intake, with self-efficacy for weight control partially accounting for this association. Self-efficacy for weight control also indirectly linked physical activity attributions and physical activity behaviour. Relationships between genetic causal beliefs, self-efficacy for weight control, and obesity-related behaviours differed by obesity status. Self-efficacy for weight control contributed to negative associations between genetic causal attributions and obesity-related behaviours in non-obese, but not obese, women. Self-efficacy is an important construct to include in studies of genetic causal beliefs and behavioural self-regulation. Theoretical and longitudinal work is needed to clarify the causal nature of these relationships and other mediating and moderating factors.
An Analysis of the Ontological Causal Relation in Physics and Its Educational Implications

ERIC Educational Resources Information Center

Cheong, Yong Wook

2016-01-01

An ontological causal relation is a quantified relation between certain interactions and changes in corresponding properties. Key ideas in physics, such as Newton's second law and the first law of thermodynamics, are representative examples of these relations. In connection with the teaching and learning of these relations, this study investigated…
Causal illusions in children when the outcome is frequent

PubMed Central

2017-01-01

Causal illusions occur when people perceive a causal relation between two events that are actually unrelated. One factor that has been shown to promote these mistaken beliefs is the outcome probability. Thus, people tend to overestimate the strength of a causal relation when the potential consequence (i.e. the outcome) occurs with a high probability (outcome-density bias). Given that children and adults differ in several important features involved in causal judgment, including prior knowledge and basic cognitive skills, developmental studies can be considered an outstanding approach to detect and further explore the psychological processes and mechanisms underlying this bias. However, the outcome density bias has been mainly explored in adulthood, and no previous evidence for this bias has been reported in children. Thus, the purpose of this study was to extend outcome-density bias research to childhood. In two experiments, children between 6 and 8 years old were exposed to two similar setups, both showing a non-contingent relation between the potential cause and the outcome. These two scenarios differed only in the probability of the outcome, which could either be high or low. Children judged the relation between the two events to be stronger in the high probability of the outcome setting, revealing that, like adults, they develop causal illusions when the outcome is frequent. PMID:28898294
Causal strength induction from time series data.

PubMed

Soo, Kevin W; Rottman, Benjamin M

2018-04-01

One challenge when inferring the strength of cause-effect relations from time series data is that the cause and/or effect can exhibit temporal trends. If temporal trends are not accounted for, a learner could infer that a causal relation exists when it does not, or even infer that there is a positive causal relation when the relation is negative, or vice versa. We propose that learners use a simple heuristic to control for temporal trends-that they focus not on the states of the cause and effect at a given instant, but on how the cause and effect change from one observation to the next, which we call transitions. Six experiments were conducted to understand how people infer causal strength from time series data. We found that participants indeed use transitions in addition to states, which helps them to reach more accurate causal judgments (Experiments 1A and 1B). Participants use transitions more when the stimuli are presented in a naturalistic visual format than a numerical format (Experiment 2), and the effect of transitions is not driven by primacy or recency effects (Experiment 3). Finally, we found that participants primarily use the direction in which variables change rather than the magnitude of the change for estimating causal strength (Experiments 4 and 5). Collectively, these studies provide evidence that people often use a simple yet effective heuristic for inferring causal strength from time series data. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
L'optique pour la sécurité sanitaire des aliments

NASA Astrophysics Data System (ADS)

Piat, Félix

2017-12-01

Il semble aujourd'hui évident que chaque produit alimentaire commercialisé ne représente aucun danger pour la santé. Cet acquis repose pourtant sur un système complexe de règles et de contrôles appliqués quotidiennement dans les coulisses de l'industrie agro-alimentaire. Les technologies optiques y jouent un rôle prépondérant, permettant de séparer le bon grain de l'ivraie.
Tracking time-varying causality and directionality of information flow using an error reduction ratio test with applications to electroencephalography data.

PubMed

Zhao, Yifan; Billings, Steve A; Wei, Hualiang; Sarrigiannis, Ptolemaios G

2012-11-01

This paper introduces an error reduction ratio-causality (ERR-causality) test that can be used to detect and track causal relationships between two signals. In comparison to the traditional Granger method, one significant advantage of the new ERR-causality test is that it can effectively detect the time-varying direction of linear or nonlinear causality between two signals without fitting a complete model. Another important advantage is that the ERR-causality test can detect both the direction of interactions and estimate the relative time shift between the two signals. Numerical examples are provided to illustrate the effectiveness of the new method together with the determination of the causality between electroencephalograph signals from different cortical sites for patients during an epileptic seizure.
Aging and Integration of Contingency Evidence in Causal Judgment

PubMed Central

Mutter, Sharon A.; Plumlee, Leslie F.

2009-01-01

Age differences in causal judgment are consistently greater for preventative/negative relationships than for generative/positive relationships. We used a feature analytic procedure (Mandel & Lehman, 1998) to determine whether this effect might be due to differences in young and older adults’ integration of contingency evidence during causal induction. To reduce the impact of age-related changes in learning/memory we presented contingency evidence for preventative, non-contingent, and generative relationships in summary form and to induce participants to integrate greater or lesser amounts of this evidence, we varied the meaningfulness of the causal context. Young adults showed greater flexibility in their integration processes than older adults. In an abstract causal context, there were no age differences in causal judgment or integration, but in meaningful contexts, young adults’ judgments for preventative relationships were more accurate than older adults’ and they assigned more weight to the contingency evidence confirming these relationships. These differences were mediated by age-related changes in processing speed. The decline in this basic cognitive resource may place boundaries on the amount or the type of evidence that older adults can integrate for causal judgment. PMID:20025406
Causal interactions in resting-state networks predict perceived loneliness.

PubMed

Tian, Yin; Yang, Li; Chen, Sifan; Guo, Daqing; Ding, Zechao; Tam, Kin Yip; Yao, Dezhong

2017-01-01

Loneliness is broadly described as a negative emotional response resulting from the differences between the actual and desired social relations of an individual, which is related to the neural responses in connection with social and emotional stimuli. Prior research has discovered that some neural regions play a role in loneliness. However, little is known about the differences among individuals in loneliness and the relationship of those differences to differences in neural networks. The current study aimed to investigate individual differences in perceived loneliness related to the causal interactions between resting-state networks (RSNs), including the dorsal attentional network (DAN), the ventral attentional network (VAN), the affective network (AfN) and the visual network (VN). Using conditional granger causal analysis of resting-state fMRI data, we revealed that the weaker causal flow from DAN to VAN is related to higher loneliness scores, and the decreased causal flow from AfN to VN is also related to higher loneliness scores. Our results clearly support the hypothesis that there is a connection between loneliness and neural networks. It is envisaged that neural network features could play a key role in characterizing the loneliness of an individual.
Causal interactions in resting-state networks predict perceived loneliness

PubMed Central

Yang, Li; Chen, Sifan; Guo, Daqing; Ding, Zechao; Tam, Kin Yip; Yao, Dezhong

2017-01-01

Loneliness is broadly described as a negative emotional response resulting from the differences between the actual and desired social relations of an individual, which is related to the neural responses in connection with social and emotional stimuli. Prior research has discovered that some neural regions play a role in loneliness. However, little is known about the differences among individuals in loneliness and the relationship of those differences to differences in neural networks. The current study aimed to investigate individual differences in perceived loneliness related to the causal interactions between resting-state networks (RSNs), including the dorsal attentional network (DAN), the ventral attentional network (VAN), the affective network (AfN) and the visual network (VN). Using conditional granger causal analysis of resting-state fMRI data, we revealed that the weaker causal flow from DAN to VAN is related to higher loneliness scores, and the decreased causal flow from AfN to VN is also related to higher loneliness scores. Our results clearly support the hypothesis that there is a connection between loneliness and neural networks. It is envisaged that neural network features could play a key role in characterizing the loneliness of an individual. PMID:28545125
Children's Counterfactual Reasoning About Causally Overdetermined Events.

PubMed

Nyhout, Angela; Henke, Lena; Ganea, Patricia A

2017-08-07

In two experiments, one hundred and sixty-two 6- to 8-year-olds were asked to reason counterfactually about events with different causal structures. All events involved overdetermined outcomes in which two different causal events led to the same outcome. In Experiment 1, children heard stories with either an ambiguous causal relation between events or causally unrelated events. Children in the causally unrelated version performed better than chance and better than those in the ambiguous condition. In Experiment 2, children heard stories in which antecedent events were causally connected or causally disconnected. Eight-year-olds performed above chance in both conditions, whereas 6-year-olds performed above chance only in the connected condition. This work provides the first evidence that children can reason counterfactually in causally overdetermined contexts by age 8. © 2017 The Authors. Child Development © 2017 Society for Research in Child Development, Inc.
Genetic causal beliefs about obesity, self-efficacy for weight control, and obesity-related behaviours in a middle-aged female cohort

PubMed Central

Knerr, Sarah; Bowen, Deborah J.; Beresford, Shirley A.A.; Wang, Catharine

2015-01-01

Objective Obesity is a heritable condition with well-established risk-reducing behaviours. Studies have shown that beliefs about the causes of obesity are associated with diet and exercise behaviour. Identifying mechanisms linking causal beliefs and behaviours is important for obesity prevention and control. Design Cross-sectional multi-level regression analyses of self-efficacy for weight control as a possible mediator of obesity attributions (diet, physical activity, genetic) and preventive behaviours in 487 non-Hispanic White women from South King County, Washington. Main Outcome Measures Self-reported daily fruit and vegetable intake and weekly leisure-time physical activity. Results Diet causal beliefs were positively associated with fruit and vegetable intake, with self-efficacy for weight control partially accounting for this association. Self-efficacy for weight control also indirectly linked physical activity attributions and physical activity behaviour. Relationships between genetic causal beliefs, self-efficacy for weight control, and obesity-related behaviours differed by obesity status. Self-efficacy for weight control contributed to negative associations between genetic causal attributions and obesity-related behaviours in non-obese, but not obese, women. Conclusion Self-efficacy is an important construct to include in studies of genetic causal beliefs and behavioural self-regulation. Theoretical and longitudinal work is needed to clarify the causal nature of these relationships and other mediating and moderating factors. PMID:26542069
Illness causal beliefs in Turkish immigrants

PubMed Central

Minas, Harry; Klimidis, Steven; Tuncer, Can

2007-01-01

Background People hold a wide variety of beliefs concerning the causes of illness. Such beliefs vary across cultures and, among immigrants, may be influenced by many factors, including level of acculturation, gender, level of education, and experience of illness and treatment. This study examines illness causal beliefs in Turkish-immigrants in Australia. Methods Causal beliefs about somatic and mental illness were examined in a sample of 444 members of the Turkish population of Melbourne. The socio-demographic characteristics of the sample were broadly similar to those of the Melbourne Turkish community. Five issues were examined: the structure of causal beliefs; the relative frequency of natural, supernatural and metaphysical beliefs; ascription of somatic, mental, or both somatic and mental conditions to the various causes; the correlations of belief types with socio-demographic, modernizing and acculturation variables; and the relationship between causal beliefs and current illness. Results Principal components analysis revealed two broad factors, accounting for 58 percent of the variation in scores on illness belief scales, distinctly interpretable as natural and supernatural beliefs. Second, beliefs in natural causes were more frequent than beliefs in supernatural causes. Third, some causal beliefs were commonly linked to both somatic and mental conditions while others were regarded as more specific to either somatic or mental disorders. Last, there was a range of correlations between endorsement of belief types and factors defining heterogeneity within the community, including with demographic factors, indicators of modernizing and acculturative processes, and the current presence of illness. Conclusion Results supported the classification of causal beliefs proposed by Murdock, Wilson & Frederick, with a division into natural and supernatural causes. While belief in natural causes is more common, belief in supernatural causes persists despite modernizing and acculturative influences. Different types of causal beliefs are held in relation to somatic or mental illness, and a variety of apparently logically incompatible beliefs may be concurrently held. Illness causal beliefs are dynamic and are related to demographic, modernizing, and acculturative factors, and to the current presence of illness. Any assumption of uniformity of illness causal beliefs within a community, even one that is relatively culturally homogeneous, is likely to be misleading. A better understanding of the diversity, and determinants, of illness causal beliefs can be of value in improving our understanding of illness experience, the clinical process, and in developing more effective health services and population health strategies. PMID:17645806
Illness causal beliefs in Turkish immigrants.

PubMed

Minas, Harry; Klimidis, Steven; Tuncer, Can

2007-07-24

People hold a wide variety of beliefs concerning the causes of illness. Such beliefs vary across cultures and, among immigrants, may be influenced by many factors, including level of acculturation, gender, level of education, and experience of illness and treatment. This study examines illness causal beliefs in Turkish-immigrants in Australia. Causal beliefs about somatic and mental illness were examined in a sample of 444 members of the Turkish population of Melbourne. The socio-demographic characteristics of the sample were broadly similar to those of the Melbourne Turkish community. Five issues were examined: the structure of causal beliefs; the relative frequency of natural, supernatural and metaphysical beliefs; ascription of somatic, mental, or both somatic and mental conditions to the various causes; the correlations of belief types with socio-demographic, modernizing and acculturation variables; and the relationship between causal beliefs and current illness. Principal components analysis revealed two broad factors, accounting for 58 percent of the variation in scores on illness belief scales, distinctly interpretable as natural and supernatural beliefs. Second, beliefs in natural causes were more frequent than beliefs in supernatural causes. Third, some causal beliefs were commonly linked to both somatic and mental conditions while others were regarded as more specific to either somatic or mental disorders. Last, there was a range of correlations between endorsement of belief types and factors defining heterogeneity within the community, including with demographic factors, indicators of modernizing and acculturative processes, and the current presence of illness. Results supported the classification of causal beliefs proposed by Murdock, Wilson & Frederick, with a division into natural and supernatural causes. While belief in natural causes is more common, belief in supernatural causes persists despite modernizing and acculturative influences. Different types of causal beliefs are held in relation to somatic or mental illness, and a variety of apparently logically incompatible beliefs may be concurrently held. Illness causal beliefs are dynamic and are related to demographic, modernizing, and acculturative factors, and to the current presence of illness. Any assumption of uniformity of illness causal beliefs within a community, even one that is relatively culturally homogeneous, is likely to be misleading. A better understanding of the diversity, and determinants, of illness causal beliefs can be of value in improving our understanding of illness experience, the clinical process, and in developing more effective health services and population health strategies.

The Model Analyst’s Toolkit: Scientific Model Development, Analysis, and Validation

DTIC Science & Technology

2015-02-20

being integrated within MAT, including Granger causality. Granger causality tests whether a data series helps when predicting future values of another...relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 424-438. Granger, C. W. (1980). Testing ... testing dataset. This effort is described in Section 3.2. 3.1. Improvements in Granger Causality User Interface Various metrics of causality are
Who Is the Dynamic Duo? How Infants Learn about the Identity of Objects in a Causal Chain

ERIC Educational Resources Information Center

Rakison, David H.; Smith, Gabriel Tobin; Ali, Areej

2016-01-01

Four experiments investigated infants' and adults' knowledge of the identity of objects in a causal sequence of events. In Experiments 1 and 2, 18- and 22-month-olds in the visual habituation procedure were shown a 3-step causal chain event in which the relation between an object's part (dynamic or static) and its causal role was either consistent…
Causal Learning in Gambling Disorder: Beyond the Illusion of Control.

PubMed

Perales, José C; Navas, Juan F; Ruiz de Lara, Cristian M; Maldonado, Antonio; Catena, Andrés

2017-06-01

Causal learning is the ability to progressively incorporate raw information about dependencies between events, or between one's behavior and its outcomes, into beliefs of the causal structure of the world. In spite of the fact that some cognitive biases in gambling disorder can be described as alterations of causal learning involving gambling-relevant cues, behaviors, and outcomes, general causal learning mechanisms in gamblers have not been systematically investigated. In the present study, we compared gambling disorder patients against controls in an instrumental causal learning task. Evidence of illusion of control, namely, overestimation of the relationship between one's behavior and an uncorrelated outcome, showed up only in gamblers with strong current symptoms. Interestingly, this effect was part of a more complex pattern, in which gambling disorder patients manifested a poorer ability to discriminate between null and positive contingencies. Additionally, anomalies were related to gambling severity and current gambling disorder symptoms. Gambling-related biases, as measured by a standard psychometric tool, correlated with performance in the causal learning task, but not in the expected direction. Indeed, performance of gamblers with stronger biases tended to resemble the one of controls, which could imply that anomalies of causal learning processes play a role in gambling disorder, but do not seem to underlie gambling-specific biases, at least in a simple, direct way.
Imputation of adverse drug reactions: Causality assessment in hospitals

PubMed Central

Mastroianni, Patricia de Carvalho

2017-01-01

Background & objectives Different algorithms have been developed to standardize the causality assessment of adverse drug reactions (ADR). Although most share common characteristics, the results of the causality assessment are variable depending on the algorithm used. Therefore, using 10 different algorithms, the study aimed to compare inter-rater and multi-rater agreement for ADR causality assessment and identify the most consistent to hospitals. Methods Using ten causality algorithms, four judges independently assessed the first 44 cases of ADRs reported during the first year of implementation of a risk management service in a medium complexity hospital in the state of Sao Paulo (Brazil). Owing to variations in the terminology used for causality, the equivalent imputation terms were grouped into four categories: definite, probable, possible and unlikely. Inter-rater and multi-rater agreement analysis was performed by calculating the Cohen´s and Light´s kappa coefficients, respectively. Results None of the algorithms showed 100% reproducibility in the causal imputation. Fair inter-rater and multi-rater agreement was found. Emanuele (1984) and WHO-UMC (2010) algorithms showed a fair rate of agreement between the judges (k = 0.36). Interpretation & conclusions Although the ADR causality assessment algorithms were poorly reproducible, our data suggest that WHO-UMC algorithm is the most consistent for imputation in hospitals, since it allows evaluating the quality of the report. However, to improve the ability of assessing the causality using algorithms, it is necessary to include criteria for the evaluation of drug-related problems, which may be related to confounding variables that underestimate the causal association. PMID:28166274
Basic Language Skills and Young Children's Understanding of Causal Connections during Storytelling

ERIC Educational Resources Information Center

Brown, Danielle D.; Lile, Jacquelyn; Burns, Barbara M.

2011-01-01

The current study examined the role of basic language skills for individual differences in preschoolers' understanding of causal connections. Assessments of basic language skills, expressive vocabulary, phonological processing, and receptive language comprehension were examined in relation to the production of causal connections in a storytelling…
Thinking Fast and Slow about Causality: Response to Palinkas

ERIC Educational Resources Information Center

Marsh, Jeanne C.

2014-01-01

Larry Palinkas advances the developing science of social work by providing an explanation of how social science research methods, both qualitative and quantitative, can improve our capacity to draw casual inferences. Understanding causal relations and making causal inferences--with the promise of being able to predict and control outcomes--is…
Introducing Causality and Power into Family Therapy Theory: A Correction to the Systemic Paradigm.

ERIC Educational Resources Information Center

Fish, Vincent

1990-01-01

Proposes that concepts of causality and power are compatible with systemic paradigm based on cybernetics of Ashby rather than that of Bateson. Criticizes Bateson's repudiation of causality and power; addresses related Batesonian biases against "quantity" and "logic." Contrasts relevant aspects of Ashby's cybernetic theory with…
Feature Inference and the Causal Structure of Categories

ERIC Educational Resources Information Center

Rehder, B.; Burnett, R.C.

2005-01-01

The purpose of this article was to establish how theoretical category knowledge-specifically, knowledge of the causal relations that link the features of categories-supports the ability to infer the presence of unobserved features. Our experiments were designed to test proposals that causal knowledge is represented psychologically as Bayesian…
An Efficient Two-Tier Causal Protocol for Mobile Distributed Systems

PubMed Central

Dominguez, Eduardo Lopez; Pomares Hernandez, Saul E.; Gomez, Gustavo Rodriguez; Medina, Maria Auxilio

2013-01-01

Causal ordering is a useful tool for mobile distributed systems (MDS) to reduce the non-determinism induced by three main aspects: host mobility, asynchronous execution, and unpredictable communication delays. Several causal protocols for MDS exist. Most of them, in order to reduce the overhead and the computational cost over wireless channels and mobile hosts (MH), ensure causal ordering at and according to the causal view of the Base Stations. Nevertheless, these protocols introduce certain disadvantage, such as unnecessary inhibition at the delivery of messages. In this paper, we present an efficient causal protocol for groupware that satisfies the MDS's constraints, avoiding unnecessary inhibitions and ensuring the causal delivery based on the view of the MHs. One interesting aspect of our protocol is that it dynamically adapts the causal information attached to each message based on the number of messages with immediate dependency relation, and this is not directly proportional to the number of MHs. PMID:23585828
Causal discovery and inference: concepts and recent methodological advances.

PubMed

Spirtes, Peter; Zhang, Kun

This paper aims to give a broad coverage of central concepts and principles involved in automated causal inference and emerging approaches to causal discovery from i.i.d data and from time series. After reviewing concepts including manipulations, causal models, sample predictive modeling, causal predictive modeling, and structural equation models, we present the constraint-based approach to causal discovery, which relies on the conditional independence relationships in the data, and discuss the assumptions underlying its validity. We then focus on causal discovery based on structural equations models, in which a key issue is the identifiability of the causal structure implied by appropriately defined structural equation models: in the two-variable case, under what conditions (and why) is the causal direction between the two variables identifiable? We show that the independence between the error term and causes, together with appropriate structural constraints on the structural equation, makes it possible. Next, we report some recent advances in causal discovery from time series. Assuming that the causal relations are linear with nonGaussian noise, we mention two problems which are traditionally difficult to solve, namely causal discovery from subsampled data and that in the presence of confounding time series. Finally, we list a number of open questions in the field of causal discovery and inference.
Translating context to causality in cardiovascular disparities research.

PubMed

Benn, Emma K T; Goldfeld, Keith S

2016-04-01

Moving from a descriptive focus to a comprehensive analysis grounded in causal inference can be particularly daunting for disparities researchers. However, even a simple model supported by the theoretical underpinnings of causality gives researchers a better chance to make correct inferences about possible interventions that can benefit our most vulnerable populations. This commentary provides a brief description of how race/ethnicity and context relate to questions of causality, and uses a hypothetical scenario to explore how different researchers might analyze the data to estimate causal effects of interest. Perhaps although not entirely removed of bias, these causal estimates will move us a step closer to understanding how to intervene. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Relations between causal attributions for stuttering and psychological well-being in adults who stutter.

PubMed

Boyle, Michael

2016-02-01

This study attempted to understand the relationship between causal attributions for stuttering and psychological well-being in adults who stutter. The study employed a cross-sectional design using a web survey distribution mode to gain information related to causal attributions and psychological well-being of 348 adults who stutter. Correlation analyses were conducted to determine relationships between participants' causal attributions (i.e. locus of causality, external control, personal control, stability, biological attributions, non-biological attributions) for stuttering and various measures of psychological well-being including self-stigma, self-esteem/self-efficacy, hope, anxiety and depression. Results indicated that higher perceptions of external control of stuttering were related to significantly lower ratings of hope and self-esteem/self-efficacy and higher ratings of anxiety and depression. Higher perceptions of personal control of stuttering were related to significantly lower ratings of self-stigma and higher ratings of hope and self-esteem/self-efficacy. Increased biological attributions were significantly related to higher ratings of permanency and unchangeableness of stuttering and lower ratings of personal control of stuttering. The findings demonstrate the importance of instilling a sense of control in PWS regarding their ability to manage their stuttering. Findings also raise questions regarding the benefits of educating PWS about the biological underpinnings of stuttering.
Learning About Causes From People: Observational Causal Learning in 24-Month-Old Infants

PubMed Central

Meltzoff, Andrew N.; Waismeyer, Anna; Gopnik, Alison

2013-01-01

How do infants and young children learn about the causal structure of the world around them? In 4 experiments we investigate whether young children initially give special weight to the outcomes of goal-directed interventions they see others perform and use this to distinguish correlations from genuine causal relations—observational causal learning. In a new 2-choice procedure, 2- to 4-year-old children saw 2 identical objects (potential causes). Activation of 1 but not the other triggered a spatially remote effect. Children systematically intervened on the causal object and predictively looked to the effect. Results fell to chance when the cause and effect were temporally reversed, so that the events were merely associated but not causally related. The youngest children (24- to 36-month-olds) were more likely to make causal inferences when covariations were the outcome of human interventions than when they were not. Observational causal learning may be a fundamental learning mechanism that enables infants to abstract the causal structure of the world. PMID:22369335
A Tutorial in Bayesian Potential Outcomes Mediation Analysis.

PubMed

Miočević, Milica; Gonzalez, Oscar; Valente, Matthew J; MacKinnon, David P

2018-01-01

Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.
A call for theory to support the use of causal-formative indicators: A commentary on Bollen and Diamantopoulos (2017).

PubMed

Hardin, Andrew

2017-09-01

In this issue, Bollen and Diamantopoulos (2017) defend causal-formative indicators against several common criticisms leveled by scholars who oppose their use. In doing so, the authors make several convincing assertions: Constructs exist independently from their measures; theory determines whether indicators cause or measure latent variables; and reflective and causal-formative indicators are both subject to interpretational confounding. However, despite being a well-reasoned, comprehensive defense of causal-formative indicators, no single article can address all of the issues associated with this debate. Thus, Bollen and Diamantopoulos leave a few fundamental issues unresolved. For example, how can researchers establish the reliability of indicators that may include measurement error? Moreover, how should researchers interpret disturbance terms that capture sources of influence related to both the empirical definition of the latent variable and to the theoretical definition of the construct? Relatedly, how should researchers reconcile the requirement for a census of causal-formative indicators with the knowledge that indicators are likely missing from the empirically estimated latent variable? This commentary develops 6 related research questions to draw attention to these fundamental issues, and to call for future research that can lead to the development of theory to guide the use of causal-formative indicators. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
The Feasibility of Using Causal Indicators in Educational Measurement

ERIC Educational Resources Information Center

Wang, Jue; Engelhard, George, Jr.

2016-01-01

The authors of the focus article describe an important issue related to the use and interpretation of causal indicators within the context of structural equation modeling (SEM). In the focus article, the authors illustrate with simulated data the effects of omitting a causal indicator. Since SEMs are used extensively in the social and behavioral…
Manifest Variable Granger Causality Models for Developmental Research: A Taxonomy

ERIC Educational Resources Information Center

von Eye, Alexander; Wiedermann, Wolfgang

2015-01-01

Granger models are popular when it comes to testing hypotheses that relate series of measures causally to each other. In this article, we propose a taxonomy of Granger causality models. The taxonomy results from crossing the four variables Order of Lag, Type of (Contemporaneous) Effect, Direction of Effect, and Segment of Dependent Series…
Cross-Lagged Associations between Kindergarten Teachers' Causal Attributions and Children's Task Motivation and Performance in Reading

ERIC Educational Resources Information Center

Natale, Katja; Viljaranta, Jaana; Lerkkanen, Marja-Kristiina; Poikkeus, Anna-Maija; Nurmi, Jari-Erik

2009-01-01

The present study investigated whether kindergarten teachers' causal attributions would predict children's reading-related task motivation and performance, or whether it is rather children's motivation and performance that contribute to teachers' causal attributions. To investigate this, 69 children (five to six years old at baseline) and their…
Exploring Work-Related Causal Attributions of Common Mental Disorders.

PubMed

Olsen, Ingrid Blø; Øverland, Simon; Reme, Silje Endresen; Løvvik, Camilla

2015-09-01

Common mental disorders (CMDs) are major causes of sickness absence and disability. Prevention requires knowledge of how individuals perceive causal mechanisms, and in this study we sought to examine work-related factors as causal attribution of CMDs. A trial sample of n = 1,193, recruited because they struggled with work participation due to CMDs, answered an open-ended questionnaire item about what they believed were the most important causes of their CMDs. The population included participants at risk of sickness absence, and participants with reduced work participation due to sickness absence, disability or unemployment. We used thematic content analysis and categorized responses from 487 participants who reported work-related factors as causal attributions of their CMDs. Gender differences in work-related causal attributions were also examined. The participants attributed their CMDs to the following work-related factors; work stress, leadership, reduced work participation, job dissatisfaction, work conflict, social work environment, job insecurity and change, workplace bullying, and physical strain. Women tended to attribute CMDs to social factors at work. Findings from this study suggest several work-related risk factors for CMDs. Both factors at the workplace, and reduced work participation, were perceived by study participants as contributing causes of CMDs. Thus, there is a need to promote work participation whilst at the same time targeting aversive workplace factors. Further, our findings indicate that work-related factors may affect women and men differently. This illustrates that the association between work participation and CMDs is complex, and needs to be explored further.
Anticipating explanations in relative clause processing.

PubMed

Rohde, H; Levy, R; Kehler, A

2011-03-01

We show that comprehenders' expectations about upcoming discourse coherence relations influence the resolution of local structural ambiguity. We employ cases in which two clauses share both a syntactic relationship and a discourse relationship, and hence in which syntactic and discourse processing might be expected to interact. An off-line sentence-completion study and an on-line self-paced reading study examined readers' expectations for high/low relative-clause attachments following implicit-causality and non-implicit causality verbs (John detests/babysits the children of the musician who…). In the off-line study, the widely reported low-attachment preference for English is observed in the non-implicit causality condition, but this preference gives way to more high attachments in the implicit-causality condition in cases in which (i) the verb's causally implicated referent occupies the high-attachment position and (ii) the relative clause provides an explanation for the event described by the matrix clause (e.g., …who are arrogant and rude). In the on-line study, a similar preference for high attachment emerges in the implicit-causality context-crucially, before the occurrence of any linguistic evidence that the RC does in fact provide an explanation-whereas the low-attachment preference is consistent elsewhere. These findings constitute the first demonstration that expectations about ensuing discourse coherence relationships can elicit full reversals in syntactic attachment preferences, and that these discourse-level expectations can affect on-line disambiguation as rapidly as lexical and morphosyntactic cues. Copyright © 2010 Elsevier B.V. All rights reserved.

Anticipating Explanations in Relative Clause Processing

PubMed Central

Rohde, H.; Levy, R.; Kehler, A.

2011-01-01

We show that comprehenders’ expectations about upcoming discourse coherence relations influence the resolution of local structural ambiguity. We employ cases in which two clauses share both a syntactic relationship and a discourse relationship, and hence in which syntactic and discourse processing might be expected to interact. An off-line sentence-completion study and an on-line self-paced reading study examined readers’ expectations for high/low relative clause attachments following implicit-causality and non-implicit-causality verbs (John detests/babysits the children of the musician who…). In the off-line study, the widely reported low-attachment preference for English is observed in the non-implicit causality condition, but this preference gives way to more high attachments in the implicit causality condition in cases in which (i) the verb’s causally implicated referent occupies the high-attachment position and (ii) the relative clause provides an explanation for the event described by the matrix clause (e.g., …who are arrogant and rude). In the on-line study, a similar preference for high attachment emerges in the implicit causality context—crucially, before the occurrence of any linguistic evidence that the RC does in fact provide an explanation—whereas the low-attachment preference is consistent elsewhere. These findings constitute the first demonstration that expectations about ensuing discourse coherence relationships can elicit full reversals in syntactic attachment preferences, and that these discourse-level expectations can affect on-line disambiguation as rapidly as lexical and morphosyntactic cues. PMID:21216396
Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks

PubMed Central

2018-01-01

Signaling pathways represent parts of the global biological molecular network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during development or in pathological conditions. We suggest a novel methodology, called Googlomics, for the structural analysis of directed biological networks using spectral analysis of their Google matrices, using parallels with quantum scattering theory, developed for nuclear and mesoscopic physics and quantum chaos. We introduce analytical “reduced Google matrix” method for the analysis of biological network structure. The method allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as a result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach rigorously characterizes complex systemic changes in the wiring of large causal biological networks in a computationally efficient way. PMID:29370181
The influence of the number of relevant causes on the processing of covariation information in causal reasoning.

PubMed

Kim, Kyungil; Markman, Arthur B; Kim, Tae Hoon

2016-11-01

Research on causal reasoning has focused on the influence of covariation between candidate causes and effects on causal judgments. We suggest that the type of covariation information to which people attend is affected by the task being performed. For this, we manipulated the test questions for the evaluation of contingency information and observed its influence on both contingency learning and subsequent causal selections. When people select one cause related to an effect, they focus on conditional contingencies assuming the absence of alternative causes. When people select two causes related to an effect, they focus on conditional contingencies assuming the presence of alternative causes. We demonstrated this use of contingency information in four experiments.
Research on injury compensation and health outcomes: ignoring the problem of reverse causality led to a biased conclusion.

PubMed

Spearing, Natalie M; Connelly, Luke B; Nghiem, Hong S; Pobereskin, Louis

2012-11-01

This study highlights the serious consequences of ignoring reverse causality bias in studies on compensation-related factors and health outcomes and demonstrates a technique for resolving this problem of observational data. Data from an English longitudinal study on factors, including claims for compensation, associated with recovery from neck pain (whiplash) after rear-end collisions are used to demonstrate the potential for reverse causality bias. Although it is commonly believed that claiming compensation leads to worse recovery, it is also possible that poor recovery may lead to compensation claims--a point that is seldom considered and never addressed empirically. This pedagogical study compares the association between compensation claiming and recovery when reverse causality bias is ignored and when it is addressed, controlling for the same observable factors. When reverse causality is ignored, claimants appear to have a worse recovery than nonclaimants; however, when reverse causality bias is addressed, claiming compensation appears to have a beneficial effect on recovery, ceteris paribus. To avert biased policy and judicial decisions that might inadvertently disadvantage people with compensable injuries, there is an urgent need for researchers to address reverse causality bias in studies on compensation-related factors and health. Copyright © 2012 Elsevier Inc. All rights reserved.
Testing concordance of instrumental variable effects in generalized linear models with application to Mendelian randomization

PubMed Central

Dai, James Y.; Chan, Kwun Chuen Gary; Hsu, Li

2014-01-01

Instrumental variable regression is one way to overcome unmeasured confounding and estimate causal effect in observational studies. Built on structural mean models, there has been considerale work recently developed for consistent estimation of causal relative risk and causal odds ratio. Such models can sometimes suffer from identification issues for weak instruments. This hampered the applicability of Mendelian randomization analysis in genetic epidemiology. When there are multiple genetic variants available as instrumental variables, and causal effect is defined in a generalized linear model in the presence of unmeasured confounders, we propose to test concordance between instrumental variable effects on the intermediate exposure and instrumental variable effects on the disease outcome, as a means to test the causal effect. We show that a class of generalized least squares estimators provide valid and consistent tests of causality. For causal effect of a continuous exposure on a dichotomous outcome in logistic models, the proposed estimators are shown to be asymptotically conservative. When the disease outcome is rare, such estimators are consistent due to the log-linear approximation of the logistic function. Optimality of such estimators relative to the well-known two-stage least squares estimator and the double-logistic structural mean model is further discussed. PMID:24863158
CADDIS Related Links

EPA Pesticide Factsheets

The Causal Analysis/Diagnosis Decision Information System, or CADDIS, is a website developed to help scientists and engineers in the Regions, States, and Tribes conduct causal assessments in aquatic systems.
Instrumental Variable Analysis with a Nonlinear Exposure–Outcome Relationship

PubMed Central

Davies, Neil M.; Thompson, Simon G.

2014-01-01

Background: Instrumental variable methods can estimate the causal effect of an exposure on an outcome using observational data. Many instrumental variable methods assume that the exposure–outcome relation is linear, but in practice this assumption is often in doubt, or perhaps the shape of the relation is a target for investigation. We investigate this issue in the context of Mendelian randomization, the use of genetic variants as instrumental variables. Methods: Using simulations, we demonstrate the performance of a simple linear instrumental variable method when the true shape of the exposure–outcome relation is not linear. We also present a novel method for estimating the effect of the exposure on the outcome within strata of the exposure distribution. This enables the estimation of localized average causal effects within quantile groups of the exposure or as a continuous function of the exposure using a sliding window approach. Results: Our simulations suggest that linear instrumental variable estimates approximate a population-averaged causal effect. This is the average difference in the outcome if the exposure for every individual in the population is increased by a fixed amount. Estimates of localized average causal effects reveal the shape of the exposure–outcome relation for a variety of models. These methods are used to investigate the relations between body mass index and a range of cardiovascular risk factors. Conclusions: Nonlinear exposure–outcome relations should not be a barrier to instrumental variable analyses. When the exposure–outcome relation is not linear, either a population-averaged causal effect or the shape of the exposure–outcome relation can be estimated. PMID:25166881
Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Barrett, Adam B.; Seth, Anil K.

2009-12-01

Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. Developed originally in the field of econometrics, it has since found application in a broader arena, particularly in neuroscience. More recently transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes, has gained traction in a similarly wide field. While it has been recognized that the two concepts must be related, the exact relationship has until now not been formally described. Here we show that for Gaussian variables, Granger causality and transfer entropy are entirely equivalent, thus bridging autoregressive and information-theoretic approaches to data-driven causal inference.
The Interplay of Implicit Causality, Structural Heuristics, and Anaphor Type in Ambiguous Pronoun Resolution

ERIC Educational Resources Information Center

Järvikivi, Juhani; van Gompel, Roger P. G.; Hyönä, Jukka

2017-01-01

Two visual-world eye-tracking experiments investigating pronoun resolution in Finnish examined the time course of implicit causality information relative to both grammatical role and order-of-mention information. Experiment 1 showed an effect of implicit causality that appeared at the same time as the first-mention preference. Furthermore, when we…
Causal learning is collaborative: Examining explanation and exploration in social contexts.

PubMed

Legare, Cristine H; Sobel, David M; Callanan, Maureen

2017-10-01

Causal learning in childhood is a dynamic and collaborative process of explanation and exploration within complex physical and social environments. Understanding how children learn causal knowledge requires examining how they update beliefs about the world given novel information and studying the processes by which children learn in collaboration with caregivers, educators, and peers. The objective of this article is to review evidence for how children learn causal knowledge by explaining and exploring in collaboration with others. We review three examples of causal learning in social contexts, which elucidate how interaction with others influences causal learning. First, we consider children's explanation-seeking behaviors in the form of "why" questions. Second, we examine parents' elaboration of meaning about causal relations. Finally, we consider parents' interactive styles with children during free play, which constrains how children explore. We propose that the best way to understand children's causal learning in social context is to combine results from laboratory and natural interactive informal learning environments.
Causal Analysis After Haavelmo

PubMed Central

Heckman, James; Pinto, Rodrigo

2014-01-01

Haavelmo's seminal 1943 and 1944 papers are the first rigorous treatment of causality. In them, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined using hypothetical models that assign variation to some of the inputs determining outcomes while holding all other inputs fixed. He thus formalized and made operational Marshall's (1890) ceteris paribus analysis. We embed Haavelmo's framework into the recursive framework of Directed Acyclic Graphs (DAGs) used in one influential recent approach to causality (Pearl, 2000) and in the related literature on Bayesian nets (Lauritzen, 1996). We compare the simplicity of an analysis of causality based on Haavelmo's methodology with the complex and nonintuitive approach used in the causal literature of DAGs—the “do-calculus” of Pearl (2009). We discuss the severe limitations of DAGs and in particular of the do-calculus of Pearl in securing identification of economic models. We extend our framework to consider models for simultaneous causality, a central contribution of Haavelmo. In general cases, DAGs cannot be used to analyze models for simultaneous causality, but Haavelmo's approach naturally generalizes to cover them. PMID:25729123
Causal inference in public health.

PubMed

Glass, Thomas A; Goodman, Steven N; Hernán, Miguel A; Samet, Jonathan M

2013-01-01

Causal inference has a central role in public health; the determination that an association is causal indicates the possibility for intervention. We review and comment on the long-used guidelines for interpreting evidence as supporting a causal association and contrast them with the potential outcomes framework that encourages thinking in terms of causes that are interventions. We argue that in public health this framework is more suitable, providing an estimate of an action's consequences rather than the less precise notion of a risk factor's causal effect. A variety of modern statistical methods adopt this approach. When an intervention cannot be specified, causal relations can still exist, but how to intervene to change the outcome will be unclear. In application, the often-complex structure of causal processes needs to be acknowledged and appropriate data collected to study them. These newer approaches need to be brought to bear on the increasingly complex public health challenges of our globalized world.
Neural correlates of continuous causal word generation.

PubMed

Wende, Kim C; Straube, Benjamin; Stratmann, Mirjam; Sommer, Jens; Kircher, Tilo; Nagels, Arne

2012-09-01

Causality provides a natural structure for organizing our experience and language. Causal reasoning during speech production is a distinct aspect of verbal communication, whose related brain processes are yet unknown. The aim of the current study was to investigate the neural mechanisms underlying the continuous generation of cause-and-effect coherences during overt word production. During fMRI data acquisition participants performed three verbal fluency tasks on identical cue words: A novel causal verbal fluency task (CVF), requiring the production of multiple reasons to a given cue word (e.g. reasons for heat are fire, sun etc.), a semantic (free association, FA, e.g. associations with heat are sweat, shower etc.) and a phonological control task (phonological verbal fluency, PVF, e.g. rhymes with heat are meat, wheat etc.). We found that, in contrast to PVF, both CVF and FA activated a left lateralized network encompassing inferior frontal, inferior parietal and angular regions, with further bilateral activation in middle and inferior as well as superior temporal gyri and the cerebellum. For CVF contrasted against FA, we found greater bold responses only in the left middle frontal cortex. Large overlaps in the neural activations during free association and causal verbal fluency indicate that the access to causal relationships between verbal concepts is at least partly based on the semantic neural network. The selective activation in the left middle frontal cortex for causal verbal fluency suggests that distinct neural processes related to cause-and-effect-relations are associated with the recruitment of middle frontal brain areas. Copyright © 2012 Elsevier Inc. All rights reserved.
Assessing the strength of cardiac and sympathetic baroreflex controls via transfer entropy during orthostatic challenge

NASA Astrophysics Data System (ADS)

Porta, Alberto; Marchi, Andrea; Bari, Vlasta; De Maria, Beatrice; Esler, Murray; Lambert, Elisabeth; Baumert, Mathias

2017-05-01

The study assesses the strength of the causal relation along baroreflex (BR) in humans during an incremental postural challenge soliciting the BR. Both cardiac BR (cBR) and sympathetic BR (sBR) were characterized via BR sequence approaches from spontaneous fluctuations of heart period (HP), systolic arterial pressure (SAP), diastolic arterial pressure (DAP) and muscle sympathetic nerve activity (MSNA). A model-based transfer entropy method was applied to quantify the strength of the coupling from SAP to HP and from DAP to MSNA. The confounding influences of respiration were accounted for. Twelve young healthy subjects (20-36 years, nine females) were sequentially tilted at 0°, 20°, 30° and 40°. We found that (i) the strength of the causal relation along the cBR increases with tilt table inclination, while that along the sBR is unrelated to it; (ii) the strength of the causal coupling is unrelated to the gain of the relation; (iii) transfer entropy indexes are significantly and positively associated with simplified causality indexes derived from BR sequence analysis. The study proves that causality indexes are complementary to traditional characterization of the BR and suggests that simple markers derived from BR sequence analysis might be fruitfully exploited to estimate causality along the BR. This article is part of the themed issue `Mathematical methods in medicine: neuroscience, cardiology and pathology'.
Formulating and Answering High-Impact Causal Questions in Physiologic Childbirth Science: Concepts and Assumptions.

PubMed

Snowden, Jonathan M; Tilden, Ellen L; Odden, Michelle C

2018-06-08

In this article, we conclude our 3-part series by focusing on several concepts that have proven useful for formulating causal questions and inferring causal effects. The process of causal inference is of key importance for physiologic childbirth science, so each concept is grounded in content related to women at low risk for perinatal complications. A prerequisite to causal inference is determining that the question of interest is causal rather than descriptive or predictive. Another critical step in defining a high-impact causal question is assessing the state of existing research for evidence of causality. We introduce 2 causal frameworks that are useful for this undertaking, Hill's causal considerations and the sufficient-component cause model. We then provide 3 steps to aid perinatal researchers in inferring causal effects in a given study. First, the researcher should formulate a rigorous and clear causal question. We introduce an example of epidural analgesia and labor progression to demonstrate this process, including the central role of temporality. Next, the researcher should assess the suitability of the given data set to answer this causal question. In randomized controlled trials, data are collected with the express purpose of answering the causal question. Investigators using observational data should also ensure that their chosen causal question is answerable with the available data. Finally, investigators should design an analysis plan that targets the causal question of interest. Some data structures (eg, time-dependent confounding by labor progress when estimating the effect of epidural analgesia on postpartum hemorrhage) require specific analytical tools to control for bias and estimate causal effects. The assumptions of consistency, exchangeability, and positivity may be especially useful in carrying out these steps. Drawing on appropriate causal concepts and considering relevant assumptions strengthens our confidence that research has reduced the likelihood of alternative explanations (eg bias, chance) and estimated a causal effect. © 2018 by the American College of Nurse-Midwives.
A critical assessment of the association between postnatal toxoplasmosis and epilepsy in immune-competent patients.

PubMed

Uzorka, J W; Arend, S M

2017-07-01

While postnatal toxoplasmosis in immune-competent patients is generally considered a self-limiting and mild illness, it has been associated with a variety of more severe clinical manifestations. The causal relation with some manifestations, e.g. myocarditis, has been microbiologically proven, but this is not unequivocally so for other reported associations, such as with epilepsy. We aimed to systematically assess causality between postnatal toxoplasmosis and epilepsy in immune-competent patients. A literature search was performed. The Bradford Hill criteria for causality were used to score selected articles for each component of causality. Using an arbitrary but defined scoring system, the maximal score was 15 points (13 for case reports). Of 704 articles, five case reports or series and five case-control studies were selected. The strongest evidence for a causal relation was provided by two case reports and one case-control study, with a maximal causality score of, respectively, 9/13, 10/13 and 10/15. The remaining studies had a median causality score of 7 (range 5-9). No selection bias was identified, but 6/10 studies contained potential confounders (it was unsure whether the infection was pre- or postnatal acquired, or immunodeficiency was not specifically excluded). Based on the evaluation of the available literature, although scanty and of limited quality, a causal relationship between postnatal toxoplasmosis and epilepsy seems possible. More definite proof requires further research, e.g. by performing Toxoplasma serology in all de novo epilepsy cases.
Complex Causal Process Diagrams for Analyzing the Health Impacts of Policy Interventions

PubMed Central

Joffe, Michael; Mindell, Jennifer

2006-01-01

Causal diagrams are rigorous tools for controlling confounding. They also can be used to describe complex causal systems, which is done routinely in communicable disease epidemiology. The use of change diagrams has advantages over static diagrams, because change diagrams are more tractable, relate better to interventions, and have clearer interpretations. Causal diagrams are a useful basis for modeling. They make assumptions explicit, provide a framework for analysis, generate testable predictions, explore the effects of interventions, and identify data gaps. Causal diagrams can be used to integrate different types of information and to facilitate communication both among public health experts and between public health experts and experts in other fields. Causal diagrams allow the use of instrumental variables, which can help control confounding and reverse causation. PMID:16449586
Helicobacter pylori infection as a cause of gastritis, duodenal ulcer, gastric cancer and nonulcer dyspepsia: a systematic overview.

PubMed

Veldhuyzen van Zanten, S J; Sherman, P M

1994-01-15

To evaluate current evidence for a causal relation between Helicobacter pylori infection and gastritis, duodenal ulcer, gastric cancer and nonulcer dyspepsia. A MEDLINE search for articles published in English between January 1983 and December 1992 with the use of MeSH terms Helicobacter pylori, gastritis, duodenal ulcer, gastric cancer, dyspepsia and clinical trial; abstracts were excluded. Six journals and Current Contents were searched manually for pertinent articles published in that time frame. Original studies with at least 25 patients, case reports and reviews that examined the relation between H. pylori and the four gastrointestinal disorders; 350 articles were on gastritis, 122 on duodenal ulcer, 44 on gastric cancer and 96 on nonulcer dyspepsia. The quality of the studies was rated independently on a four-point scale. The strength of the evidence was assessed using a six-point scale for each of the eight established guidelines for determining a causal relation. There was conclusive evidence of a causal relation between H. pylori infection and histologic gastritis. Koch's postulates for the identification of a microorganism as the causative agent of a disease were fulfilled for H. pylori as a causative agent of gastritis. There was strong evidence that H. pylori is the main cause of duodenal ulcers not induced by nonsteroidal anti-inflammatory drugs, but all of Koch's postulates were not fulfilled. There was moderate epidemiologic evidence of an association between chronic H. pylori infection and gastric cancer. There was a lack of convincing evidence of a causal association between H. pylori and nonulcer dyspepsia. The evidence supports a strong causal relation between H. pylori infection and gastritis and duodenal ulcer and a moderate relation between such infection and gastric cancer. Further studies are needed to clarify the role of H. pylori in these disorders. Thus far, there is no evidence of a causal relation between H. pylori and nonulcer dyspepsia.
Helicobacter pylori infection as a cause of gastritis, duodenal ulcer, gastric cancer and nonulcer dyspepsia: a systematic overview.

PubMed Central

Veldhuyzen van Zanten, S J; Sherman, P M

1994-01-01

OBJECTIVE: To evaluate current evidence for a causal relation between Helicobacter pylori infection and gastritis, duodenal ulcer, gastric cancer and nonulcer dyspepsia. DATA SOURCES: A MEDLINE search for articles published in English between January 1983 and December 1992 with the use of MeSH terms Helicobacter pylori, gastritis, duodenal ulcer, gastric cancer, dyspepsia and clinical trial; abstracts were excluded. Six journals and Current Contents were searched manually for pertinent articles published in that time frame. STUDY SELECTION: Original studies with at least 25 patients, case reports and reviews that examined the relation between H. pylori and the four gastrointestinal disorders; 350 articles were on gastritis, 122 on duodenal ulcer, 44 on gastric cancer and 96 on nonulcer dyspepsia. DATA EXTRACTION: The quality of the studies was rated independently on a four-point scale. The strength of the evidence was assessed using a six-point scale for each of the eight established guidelines for determining a causal relation. DATA SYNTHESIS: There was conclusive evidence of a causal relation between H. pylori infection and histologic gastritis. Koch's postulates for the identification of a microorganism as the causative agent of a disease were fulfilled for H. pylori as a causative agent of gastritis. There was strong evidence that H. pylori is the main cause of duodenal ulcers not induced by nonsteroidal anti-inflammatory drugs, but all of Koch's postulates were not fulfilled. There was moderate epidemiologic evidence of an association between chronic H. pylori infection and gastric cancer. There was a lack of convincing evidence of a causal association between H. pylori and nonulcer dyspepsia. CONCLUSIONS: The evidence supports a strong causal relation between H. pylori infection and gastritis and duodenal ulcer and a moderate relation between such infection and gastric cancer. Further studies are needed to clarify the role of H. pylori in these disorders. Thus far, there is no evidence of a causal relation between H. pylori and nonulcer dyspepsia. PMID:8287340
Testing for Granger Causality in the Frequency Domain: A Phase Resampling Method.

PubMed

Liu, Siwei; Molenaar, Peter

2016-01-01

This article introduces phase resampling, an existing but rarely used surrogate data method for making statistical inferences of Granger causality in frequency domain time series analysis. Granger causality testing is essential for establishing causal relations among variables in multivariate dynamic processes. However, testing for Granger causality in the frequency domain is challenging due to the nonlinear relation between frequency domain measures (e.g., partial directed coherence, generalized partial directed coherence) and time domain data. Through a simulation study, we demonstrate that phase resampling is a general and robust method for making statistical inferences even with short time series. With Gaussian data, phase resampling yields satisfactory type I and type II error rates in all but one condition we examine: when a small effect size is combined with an insufficient number of data points. Violations of normality lead to slightly higher error rates but are mostly within acceptable ranges. We illustrate the utility of phase resampling with two empirical examples involving multivariate electroencephalography (EEG) and skin conductance data.

Do causal concentration-response functions exist? A critical review of associational and causal relations between fine particulate matter and mortality.

PubMed

Cox, Louis Anthony Tony

2017-08-01

Concentration-response (C-R) functions relating concentrations of pollutants in ambient air to mortality risks or other adverse health effects provide the basis for many public health risk assessments, benefits estimates for clean air regulations, and recommendations for revisions to existing air quality standards. The assumption that C-R functions relating levels of exposure and levels of response estimated from historical data usefully predict how future changes in concentrations would change risks has seldom been carefully tested. This paper critically reviews literature on C-R functions for fine particulate matter (PM2.5) and mortality risks. We find that most of them describe historical associations rather than valid causal models for predicting effects of interventions that change concentrations. The few papers that explicitly attempt to model causality rely on unverified modeling assumptions, casting doubt on their predictions about effects of interventions. A large literature on modern causal inference algorithms for observational data has been little used in C-R modeling. Applying these methods to publicly available data from Boston and the South Coast Air Quality Management District around Los Angeles shows that C-R functions estimated for one do not hold for the other. Changes in month-specific PM2.5 concentrations from one year to the next do not help to predict corresponding changes in average elderly mortality rates in either location. Thus, the assumption that estimated C-R relations predict effects of pollution-reducing interventions may not be true. Better causal modeling methods are needed to better predict how reducing air pollution would affect public health.
Spatial-temporal causal modeling: a data centric approach to climate change attribution (Invited)

NASA Astrophysics Data System (ADS)

Lozano, A. C.

2010-12-01

Attribution of climate change has been predominantly based on simulations using physical climate models. These approaches rely heavily on the employed models and are thus subject to their shortcomings. Given the physical models’ limitations in describing the complex system of climate, we propose an alternative approach to climate change attribution that is data centric in the sense that it relies on actual measurements of climate variables and human and natural forcing factors. We present a novel class of methods to infer causality from spatial-temporal data, as well as a procedure to incorporate extreme value modeling into our methodology in order to address the attribution of extreme climate events. We develop a collection of causal modeling methods using spatio-temporal data that combine graphical modeling techniques with the notion of Granger causality. “Granger causality” is an operational definition of causality from econometrics, which is based on the premise that if a variable causally affects another, then the past values of the former should be helpful in predicting the future values of the latter. In its basic version, our methodology makes use of the spatial relationship between the various data points, but treats each location as being identically distributed and builds a unique causal graph that is common to all locations. A more flexible framework is then proposed that is less restrictive than having a single causal graph common to all locations, while avoiding the brittleness due to data scarcity that might arise if one were to independently learn a different graph for each location. The solution we propose can be viewed as finding a middle ground by partitioning the locations into subsets that share the same causal structures and pooling the observations from all the time series belonging to the same subset in order to learn more robust causal graphs. More precisely, we make use of relationships between locations (e.g. neighboring relationship) by defining a relational graph in which related locations are connected (note that this relational graph, which represents relationships among the different locations, is distinct from the causal graph, which represents causal relationships among the individual variables - e.g. temperature, pressure- within a multivariate time series). We then define a hidden Markov Random Field (hMRF), assigning a hidden state to each node (location), with the state assignment guided by the prior information encoded in the relational graph. Nodes that share the same state in the hMRF model will have the same causal graph. State assignment can thus shed light on unknown relations among locations (e.g. teleconnection). While the model has been described in terms of hard location partitioning to facilitate its exposition, in fact a soft partitioning is maintained throughout learning. This leads to a form of transfer learning, which makes our model applicable even in situations where partitioning the locations might not seem appropriate. We first validate the effectiveness of our methodology on synthetic datasets, and then apply it to actual climate measurement data. The experimental results show that our approach offers a useful alternative to the simulation-based approach for climate modeling and attribution, and has the capability to provide valuable scientific insights from a new perspective.
A Historical Overview and Contemporary Expansion of Psychological Theories of Determinism, Probabilistic Causality, Indeterminate Free Will, and Moral and Legal Responsibility

ERIC Educational Resources Information Center

Wilks, Duffy; Ratheal, Juli D'Ann

2009-01-01

The authors provide a historical overview of the development of contemporary theories of counseling and psychology in relation to determinism, probabilistic causality, indeterminate free will, and moral and legal responsibility. They propose a unique model of behavioral causality that incorporates a theory of indeterminate free will, a concept…
Bayes Nets and Babies: Infants' Developing Statistical Reasoning Abilities and Their Representation of Causal Knowledge

ERIC Educational Resources Information Center

Sobel, David M.; Kirkham, Natasha Z.

2007-01-01

A fundamental assumption of the causal graphical model framework is the Markov assumption, which posits that learners can discriminate between two events that are dependent because of a direct causal relation between them and two events that are independent conditional on the value of another event(s). Sobel and Kirkham (2006) demonstrated that…
Physics and Causation

NASA Astrophysics Data System (ADS)

Esfeld, Michael

2010-10-01

The paper makes a case for there being causation in the form of causal properties or causal structures in the domain of fundamental physics. That case is built in the first place on an interpretation of quantum theory in terms of state reductions so that there really are both entangled states and classical properties, GRW being the most elaborate physical proposal for such an interpretation. I then argue that the interpretation that goes back to Everett can also be read in a causal manner, the splitting of the world being conceivable as a causal process. Finally, I mention that the way in which general relativity theory conceives the metrical field opens up the way for a causal conception of the metrical properties as well.
[Historical causality and relative contemporaneity Einsteinian relativity in the historical sciences].

PubMed

Bontems, Vincent

2014-01-01

The construction of historical frame of reference based on the distinction between and articulation of phenomenological and chronological times. As it relativises the notion of simultaneity and inverts its relation to causality, the special theory of relativity can induce analogous modes of reflection on the themes of "contemporaneity" in the history of art (Panofsky) and in epistemology (Bachelard). This "relativist" method, often misunderstood, sheds light on both historical and presentist methods.
Unimpaired perception of social and physical causality, but impaired perception of animacy in high functioning children with autism.

PubMed

Congiu, Sara; Schlottmann, Anne; Ray, Elizabeth

2010-01-01

We investigated perception of social and physical causality and animacy in simple motion events, for high-functioning children with autism (CA = 13, VMA = 9.6). Children matched 14 different animations to pictures showing physical, social or non-causality. In contrast to previous work, children with autism performed at a high level similar to VMA-matched controls, recognizing physical causality in launch and social causality in reaction events. The launch deficit previously found in younger children with autism, possibly related to attentional/verbal difficulties, is apparently overcome with age. Some events involved squares moving non-rigidly, like animals. Children with autism had difficulties recognizing this, extending the biological motion literature. However, animacy prompts amplified their attributions of social causality. Thus children with autism may overcome their animacy perception deficit strategically.
Confirmatory Analytic Tests of Three Causal Models Relating Job Perceptions to Job Satisfaction.

DTIC Science & Technology

1984-12-01

Perceptions ~Job SatisfactionD I~i- Confirmatory Analysi s Precognitive Postcognitive L ft A e S T R A f T I ( C O n" " n ," , V fV f f vv r e # d o i t c e...in the causal order, and job perceptions and job satisfaction are reciprocally related; (b) a precognitive -recursive model in which job perceptions...occur after job satisfaction in the causal order and are effects but not causes of job satisfaction; and (c) a precognitive DD FOR 1473 EDITION 01O NOV
The role of causal criteria in causal inferences: Bradford Hill's "aspects of association".

PubMed

Ward, Andrew C

2009-06-17

As noted by Wesley Salmon and many others, causal concepts are ubiquitous in every branch of theoretical science, in the practical disciplines and in everyday life. In the theoretical and practical sciences especially, people often base claims about causal relations on applications of statistical methods to data. However, the source and type of data place important constraints on the choice of statistical methods as well as on the warrant attributed to the causal claims based on the use of such methods. For example, much of the data used by people interested in making causal claims come from non-experimental, observational studies in which random allocations to treatment and control groups are not present. Thus, one of the most important problems in the social and health sciences concerns making justified causal inferences using non-experimental, observational data. In this paper, I examine one method of justifying such inferences that is especially widespread in epidemiology and the health sciences generally - the use of causal criteria. I argue that while the use of causal criteria is not appropriate for either deductive or inductive inferences, they do have an important role to play in inferences to the best explanation. As such, causal criteria, exemplified by what Bradford Hill referred to as "aspects of [statistical] associations", have an indispensible part to play in the goal of making justified causal claims.
The role of causal criteria in causal inferences: Bradford Hill's "aspects of association"

PubMed Central

Ward, Andrew C

2009-01-01

As noted by Wesley Salmon and many others, causal concepts are ubiquitous in every branch of theoretical science, in the practical disciplines and in everyday life. In the theoretical and practical sciences especially, people often base claims about causal relations on applications of statistical methods to data. However, the source and type of data place important constraints on the choice of statistical methods as well as on the warrant attributed to the causal claims based on the use of such methods. For example, much of the data used by people interested in making causal claims come from non-experimental, observational studies in which random allocations to treatment and control groups are not present. Thus, one of the most important problems in the social and health sciences concerns making justified causal inferences using non-experimental, observational data. In this paper, I examine one method of justifying such inferences that is especially widespread in epidemiology and the health sciences generally – the use of causal criteria. I argue that while the use of causal criteria is not appropriate for either deductive or inductive inferences, they do have an important role to play in inferences to the best explanation. As such, causal criteria, exemplified by what Bradford Hill referred to as "aspects of [statistical] associations", have an indispensible part to play in the goal of making justified causal claims. PMID:19534788
Causal Beliefs and Effects upon Mental Illness Identification Among Chinese Immigrant Relatives of Individuals with Psychosis.

PubMed

Yang, Lawrence H; Wonpat-Borja, Ahtoy J

2012-08-01

Identifying factors that facilitate treatment for psychotic disorders among Chinese-immigrants is crucial due to delayed treatment use. Identifying causal beliefs held by relatives that might predict identification of 'mental illness' as opposed to other 'indigenous labels' may promote more effective mental health service use. We examine what effects beliefs of 'physical causes' and other non-biomedical causal beliefs ('general social causes', and 'indigenous Chinese beliefs' or culture-specific epistemologies of illness) might have on mental illness identification. Forty-nine relatives of Chinese-immigrant consumers with psychosis were sampled. Higher endorsement of 'physical causes' was associated with mental illness labeling. However among the non-biomedical causal beliefs, 'general social causes' demonstrated no relationship with mental illness identification, while endorsement of 'indigenous Chinese beliefs' showed a negative relationship. Effective treatment- and community-based psychoeducation, in addition to emphasizing biomedical models, might integrate indigenous Chinese epistemologies of illness to facilitate rapid identification of psychotic disorders and promote treatment use.
Causal Beliefs and Effects upon Mental Illness Identification Among Chinese Immigrant Relatives of Individuals with Psychosis

PubMed Central

Wonpat-Borja, Ahtoy J.

2013-01-01

Identifying factors that facilitate treatment for psychotic disorders among Chinese-immigrants is crucial due to delayed treatment use. Identifying causal beliefs held by relatives that might predict identification of ‘mental illness’ as opposed to other ‘indigenous labels’ may promote more effective mental health service use. We examine what effects beliefs of ‘physical causes’ and other non-biomedical causal beliefs (‘general social causes’, and ‘indigenous Chinese beliefs’ or culture-specific epistemologies of illness) might have on mental illness identification. Forty-nine relatives of Chinese-immigrant consumers with psychosis were sampled. Higher endorsement of ‘physical causes’ was associated with mental illness labeling. However among the non-biomedical causal beliefs, ‘general social causes’ demonstrated no relationship with mental illness identification, while endorsement of ‘indigenous Chinese beliefs’ showed a negative relationship. Effective treatment- and community-based psychoeducation, in addition to emphasizing biomedical models, might integrate indigenous Chinese epistemologies of illness to facilitate rapid identification of psychotic disorders and promote treatment use. PMID:22075770
Causal interpretation of correlational studies - Analysis of medical news on the website of the official journal for German physicians.

PubMed

Buhse, Susanne; Rahn, Anne Christin; Bock, Merle; Mühlhauser, Ingrid

2018-01-01

Media frequently draws inappropriate causal statements from observational studies. We analyzed the reporting of study results in the Medical News section of the German medical journal Deutsches Ärzteblatt (DÄ). Study design: Retrospective quantitative content analysis of randomly selected news reports and related original journal articles and press releases. A medical news report was selected if headlines comprised at least two linked variables. Two raters independently categorized the headline and text of each news report, conclusions of the abstract and full text of the related journal article, and the press release. The assessment instrument comprised five categories from 'neutral' to 'unconditionally causal'. Outcome measures: degree of matching between 1) news headlines and conclusions of the journal article, 2) headlines and text of news reports, 3) text and conclusions, and 4) headlines and press releases. We analyzed whether news headlines rated as unconditionally causal based on randomized controlled trials (RCTs). One-thousand eighty-seven medical news reports were published between April 2015 and May 2016. The final random sample comprised 176 news reports and 100 related press releases. Degree of matching: 1) 45% (79/176) for news headlines and journal article conclusions, 2) 55% (97/176) for headlines and text, 3) 53% (93/176) for text and conclusions, and 4) 41% (41/100) for headlines and press releases. Exaggerations were found in 45% (80/176) of the headlines compared to the conclusions of the related journal article. Sixty-five of 137 unconditionally causal statements of the news headlines were phrased more weakly in the subsequent news text body. Only 52 of 137 headlines (38%) categorized as unconditionally causal reported RCTs. Reporting of medical news in the DÄ medical journal is misleading. Most headlines that imply causal associations were not based on RCTs. Medical journalists should follow standards of reporting scientific study results.
Normative and descriptive accounts of the influence of power and contingency on causal judgement.

PubMed

Perales, José C; Shanks, David R

2003-08-01

The power PC theory (Cheng, 1997) is a normative account of causal inference, which predicts that causal judgements are based on the power p of a potential cause, where p is the cause-effect contingency normalized by the base rate of the effect. In three experiments we demonstrate that both cause-effect contingency and effect base-rate independently affect estimates in causal learning tasks. In Experiment 1, causal strength judgements were directly related to power p in a task in which the effect base-rate was manipulated across two positive and two negative contingency conditions. In Experiments 2 and 3 contingency manipulations affected causal estimates in several situations in which power p was held constant, contrary to the power PC theory's predictions. This latter effect cannot be explained by participants' conflation of reliability and causal strength, as Experiment 3 demonstrated independence of causal judgements and confidence. From a descriptive point of view, the data are compatible with Pearce's (1987) model, as well as with several other judgement rules, but not with the Rescorla-Wagner (Rescorla & Wagner, 1972) or power PC models.
Dynamics of Quantum Causal Structures

NASA Astrophysics Data System (ADS)

Castro-Ruiz, Esteban; Giacomini, Flaminia; Brukner, Časlav

2018-01-01

It was recently suggested that causal structures are both dynamical, because of general relativity, and indefinite, because of quantum theory. The process matrix formalism furnishes a framework for quantum mechanics on indefinite causal structures, where the order between operations of local laboratories is not definite (e.g., one cannot say whether operation in laboratory A occurs before or after operation in laboratory B ). Here, we develop a framework for "dynamics of causal structures," i.e., for transformations of process matrices into process matrices. We show that, under continuous and reversible transformations, the causal order between operations is always preserved. However, the causal order between a subset of operations can be changed under continuous yet nonreversible transformations. An explicit example is that of the quantum switch, where a party in the past affects the causal order of operations of future parties, leading to a transition from a channel from A to B , via superposition of causal orders, to a channel from B to A . We generalize our framework to construct a hierarchy of quantum maps based on transformations of process matrices and transformations thereof.
Classical Causal Models for Bell and Kochen-Specker Inequality Violations Require Fine-Tuning

NASA Astrophysics Data System (ADS)

Cavalcanti, Eric G.

2018-04-01

Nonlocality and contextuality are at the root of conceptual puzzles in quantum mechanics, and they are key resources for quantum advantage in information-processing tasks. Bell nonlocality is best understood as the incompatibility between quantum correlations and the classical theory of causality, applied to relativistic causal structure. Contextuality, on the other hand, is on a more controversial foundation. In this work, I provide a common conceptual ground between nonlocality and contextuality as violations of classical causality. First, I show that Bell inequalities can be derived solely from the assumptions of no signaling and no fine-tuning of the causal model. This removes two extra assumptions from a recent result from Wood and Spekkens and, remarkably, does not require any assumption related to independence of measurement settings—unlike all other derivations of Bell inequalities. I then introduce a formalism to represent contextuality scenarios within causal models and show that all classical causal models for violations of a Kochen-Specker inequality require fine-tuning. Thus, the quantum violation of classical causality goes beyond the case of spacelike-separated systems and already manifests in scenarios involving single systems.
Aristotle and Information Theory: A Comparison of the Influence of Causal Assumptions on Two Theories of Communication. (Janua Linguarum Series Maior 35.)

ERIC Educational Resources Information Center

Rosenfield, Lawrence William

This study sought to discover what critical apparatus would be most appropriate for observers of verbal discourse who choose to accept Aristotelian or "information theory" causal accounts of dynamic process. The major conclusions were: (1) Both causal systems employ a static grid to express relationships; but while the Aristotelian relations are…
Assessing Causality in a Complex Security Environment

DTIC Science & Technology

2015-01-01

social sciences that could genuinely benefit those students. Causality is one of these critical issues. Causality has many definitions, but we might...protests (Ivan Bandura ) Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated...relatively simple theory of what leads to a stable deter- rent relationship between two states. Mearsheimer argued that when State A fields a
Analysis of electromagnetic forces and causality in electron microscopy.

PubMed

Reyes-Coronado, Alejandro; Ortíz-Solano, Carlos Gael; Zabala, Nerea; Rivacoba, Alberto; Esquivel-Sirvent, Raúl

2018-09-01

The non-physical effects on the transverse momentum transfer from fast electrons to gold nanoparticles associated to the use of non-causal dielectric functions are studied. A direct test of the causality based on the surface Kramers-Kronig relations is presented. This test is applied to the different dielectric function used to describe gold nanostructures in electron microscopy. Copyright © 2018. Published by Elsevier B.V.
Militarism and globalization: Is there an empirical link?

PubMed

Irandoust, Manuchehr

2018-01-01

Despite the fact that previous studies have extensively investigated the causal nexus between military expenditure and economic growth in both developed and developing countries, those studies have not considered the role of globalization. The aim of this study is to examine the relationship between militarism and globalization for the top 15 military expenditure spenders over the period 1990-2012. The bootstrap panel Granger causality approach is utilized to detect the direction of causality. The results show that military expenditure and overall globalization are causally related in most of the countries under review. This implies that countries experiencing greater globalization have relatively large increases in militarization over the past 20 years. The policy implication of the findings is that greater military spending by a country increases the likelihood of military conflict in the future, the anticipation of which discourages globalization.

Partial Granger causality--eliminating exogenous inputs and latent variables.

PubMed

Guo, Shuixia; Seth, Anil K; Kendrick, Keith M; Zhou, Cong; Feng, Jianfeng

2008-07-15

Attempts to identify causal interactions in multivariable biological time series (e.g., gene data, protein data, physiological data) can be undermined by the confounding influence of environmental (exogenous) inputs. Compounding this problem, we are commonly only able to record a subset of all related variables in a system. These recorded variables are likely to be influenced by unrecorded (latent) variables. To address this problem, we introduce a novel variant of a widely used statistical measure of causality--Granger causality--that is inspired by the definition of partial correlation. Our 'partial Granger causality' measure is extensively tested with toy models, both linear and nonlinear, and is applied to experimental data: in vivo multielectrode array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of sheep. Our results demonstrate that partial Granger causality can reveal the underlying interactions among elements in a network in the presence of exogenous inputs and latent variables in many cases where the existing conditional Granger causality fails.
Whose statistical reasoning is facilitated by a causal structure intervention?

PubMed

McNair, Simon; Feeney, Aidan

2015-02-01

People often struggle when making Bayesian probabilistic estimates on the basis of competing sources of statistical evidence. Recently, Krynski and Tenenbaum (Journal of Experimental Psychology: General, 136, 430-450, 2007) proposed that a causal Bayesian framework accounts for peoples' errors in Bayesian reasoning and showed that, by clarifying the causal relations among the pieces of evidence, judgments on a classic statistical reasoning problem could be significantly improved. We aimed to understand whose statistical reasoning is facilitated by the causal structure intervention. In Experiment 1, although we observed causal facilitation effects overall, the effect was confined to participants high in numeracy. We did not find an overall facilitation effect in Experiment 2 but did replicate the earlier interaction between numerical ability and the presence or absence of causal content. This effect held when we controlled for general cognitive ability and thinking disposition. Our results suggest that clarifying causal structure facilitates Bayesian judgments, but only for participants with sufficient understanding of basic concepts in probability and statistics.
Assessment of the effect of No Stay, Bent Stay or Straight Stay when the Clothe the Soldier Rucksack is Worn with the Fragmentation Vest

DTIC Science & Technology

2006-02-01

effets de différents renforts de sac à dos (renforts droits, renforts courbés et aucun renfort) sur la...supérieures au seuil de tolérance de la peau et des muscles sous-jacents, et elles pourraient causer des lésions sur la peau et des ecchymoses. Assessment...présente évaluation portait sur cette pratique et visait à déterminer les effets de différents renforts de sac à dos (renforts droits, renforts courbés
Relations among Parental Causal Attributions and Children's Math Performance and Task Persistence

ERIC Educational Resources Information Center

Tõeväli, Paula-Karoliina; Kikas, Eve

2017-01-01

The present longitudinal study examined the cross-lagged relations between parental causal attributions of children's math success to children's ability, parental help, children's math performance and task persistence. A total of 735 children, their mothers, fathers and teachers were assessed twice--at the end of the second and the third grades.…
Causality, mediation and time: a dynamic viewpoint

PubMed Central

Aalen, Odd O; Røysland, Kjetil; Gran, Jon Michael; Ledergerber, Bruno

2012-01-01

Summary. Time dynamics are often ignored in causal modelling. Clearly, causality must operate in time and we show how this corresponds to a mechanistic, or system, understanding of causality. The established counterfactual definitions of direct and indirect effects depend on an ability to manipulate the mediator which may not hold in practice, and we argue that a mechanistic view may be better. Graphical representations based on local independence graphs and dynamic path analysis are used to facilitate communication as well as providing an overview of the dynamic relations ‘at a glance’. The relationship between causality as understood in a mechanistic and in an interventionist sense is discussed. An example using data from the Swiss HIV Cohort Study is presented. PMID:23193356
Influence analysis for high-dimensional time series with an application to epileptic seizure onset zone detection

PubMed Central

Flamm, Christoph; Graef, Andreas; Pirker, Susanne; Baumgartner, Christoph; Deistler, Manfred

2013-01-01

Granger causality is a useful concept for studying causal relations in networks. However, numerical problems occur when applying the corresponding methodology to high-dimensional time series showing co-movement, e.g. EEG recordings or economic data. In order to deal with these shortcomings, we propose a novel method for the causal analysis of such multivariate time series based on Granger causality and factor models. We present the theoretical background, successfully assess our methodology with the help of simulated data and show a potential application in EEG analysis of epileptic seizures. PMID:23354014
Illusions of causality: how they bias our everyday thinking and how they could be reduced.

PubMed

Matute, Helena; Blanco, Fernando; Yarritu, Ion; Díaz-Lago, Marcos; Vadillo, Miguel A; Barberia, Itxaso

2015-01-01

Illusions of causality occur when people develop the belief that there is a causal connection between two events that are actually unrelated. Such illusions have been proposed to underlie pseudoscience and superstitious thinking, sometimes leading to disastrous consequences in relation to critical life areas, such as health, finances, and wellbeing. Like optical illusions, they can occur for anyone under well-known conditions. Scientific thinking is the best possible safeguard against them, but it does not come intuitively and needs to be taught. Teaching how to think scientifically should benefit from better understanding of the illusion of causality. In this article, we review experiments that our group has conducted on the illusion of causality during the last 20 years. We discuss how research on the illusion of causality can contribute to the teaching of scientific thinking and how scientific thinking can reduce illusion.
Illusions of causality: how they bias our everyday thinking and how they could be reduced

PubMed Central

Matute, Helena; Blanco, Fernando; Yarritu, Ion; Díaz-Lago, Marcos; Vadillo, Miguel A.; Barberia, Itxaso

2015-01-01

Illusions of causality occur when people develop the belief that there is a causal connection between two events that are actually unrelated. Such illusions have been proposed to underlie pseudoscience and superstitious thinking, sometimes leading to disastrous consequences in relation to critical life areas, such as health, finances, and wellbeing. Like optical illusions, they can occur for anyone under well-known conditions. Scientific thinking is the best possible safeguard against them, but it does not come intuitively and needs to be taught. Teaching how to think scientifically should benefit from better understanding of the illusion of causality. In this article, we review experiments that our group has conducted on the illusion of causality during the last 20 years. We discuss how research on the illusion of causality can contribute to the teaching of scientific thinking and how scientific thinking can reduce illusion. PMID:26191014
Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets.

PubMed

Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze

2017-01-01

This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
Inductive reasoning about causally transmitted properties.

PubMed

Shafto, Patrick; Kemp, Charles; Bonawitz, Elizabeth Baraff; Coley, John D; Tenenbaum, Joshua B

2008-11-01

Different intuitive theories constrain and guide inferences in different contexts. Formalizing simple intuitive theories as probabilistic processes operating over structured representations, we present a new computational model of category-based induction about causally transmitted properties. A first experiment demonstrates undergraduates' context-sensitive use of taxonomic and food web knowledge to guide reasoning about causal transmission and shows good qualitative agreement between model predictions and human inferences. A second experiment demonstrates strong quantitative and qualitative fits to inferences about a more complex artificial food web. A third experiment investigates human reasoning about complex novel food webs where species have known taxonomic relations. Results demonstrate a double-dissociation between the predictions of our causal model and a related taxonomic model [Kemp, C., & Tenenbaum, J. B. (2003). Learning domain structures. In Proceedings of the 25th annual conference of the cognitive science society]: the causal model predicts human inferences about diseases but not genes, while the taxonomic model predicts human inferences about genes but not diseases. We contrast our framework with previous models of category-based induction and previous formal instantiations of intuitive theories, and outline challenges in developing a complete model of context-sensitive reasoning.
The influence of causal connections between symptoms on the diagnosis of mental disorders: evidence from online and offline measures.

PubMed

Flores, Amanda; Cobos, Pedro L; López, Francisco J; Godoy, Antonio; González-Martín, Estrella

2014-09-01

An experiment conducted with students and experienced clinicians demonstrated very fast and online causal reasoning in the diagnosis of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) mental disorders. The experiment also demonstrated that clinicians' causal reasoning is triggered by information that is directly related to the causal structure that explains the symptoms, such as their temporal sequence. The use of causal theories was measured through explicit, verbal diagnostic judgments and through the online registration of participants' reading times of clinical reports. To detect both online and offline causal reasoning, the consistency of clinical reports was manipulated. This manipulation was made by varying the temporal order in which different symptoms developed in hypothetical clients, and by providing explicit information about causal connections between symptoms. The temporal order of symptoms affected the clinicians' but not the students' reading times. However, offline diagnostic judgments in both groups were influenced by the consistency manipulation. Overall, our results suggest that clinicians engage in fast and online causal reasoning processes when dealing with diagnostic information concerning mental disorders, and that both clinicians and students engage in causal reasoning in diagnostic judgment tasks. PsycINFO Database Record (c) 2014 APA, all rights reserved.
An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation

PubMed Central

Ma, Sisi; Kemmeren, Patrick; Aliferis, Constantin F.; Statnikov, Alexander

2016-01-01

Reverse-engineering of causal pathways that implicate diseases and vital cellular functions is a fundamental problem in biomedicine. Discovery of the local causal pathway of a target variable (that consists of its direct causes and direct effects) is essential for effective intervention and can facilitate accurate diagnosis and prognosis. Recent research has provided several active learning methods that can leverage passively observed high-throughput data to draft causal pathways and then refine the inferred relations with a limited number of experiments. The current study provides a comprehensive evaluation of the performance of active learning methods for local causal pathway discovery in real biological data. Specifically, 54 active learning methods/variants from 3 families of algorithms were applied for local causal pathways reconstruction of gene regulation for 5 transcription factors in S. cerevisiae. Four aspects of the methods’ performance were assessed, including adjacency discovery quality, edge orientation accuracy, complete pathway discovery quality, and experimental cost. The results of this study show that some methods provide significant performance benefits over others and therefore should be routinely used for local causal pathway discovery tasks. This study also demonstrates the feasibility of local causal pathway reconstruction in real biological systems with significant quality and low experimental cost. PMID:26939894
Reducing Children’s Behavior Problems through Social Capital: A Causal Assessment

PubMed Central

López Turley, Ruth N.; Gamoran, Adam; McCarty, Alyn Turner; Fish, Rachel

2016-01-01

Behavior problems among young children have serious detrimental effects on short and long-term educational outcomes. An especially promising prevention strategy may be one that focuses on strengthening the relationships among families in schools, or social capital. However, empirical research on social capital has been constrained by conceptual and causal ambiguity. This study attempts to construct a more focused conceptualization of social capital and aims to determine the causal effects of social capital on children’s behavior. Using data from a cluster randomized trial of 52 elementary schools, we apply several multilevel models to assess the causal relationship, including intent to treat and treatment on the treated analyses. Taken together, these analyses provide stronger evidence than previous studies that social capital improves children’s behavioral outcomes and that these improvements are not simply a result of selection into social relations but result from the social relations themselves. PMID:27886729
Academic procrastination: the relationship between causal attribution styles and behavioral postponement.

PubMed

Badri Gargari, Rahim; Sabouri, Hossein; Norzad, Fatemeh

2011-01-01

This research was conducted to study the relationship between attribution and academic procrastination in University Students. The subjects were 203 undergraduate students, 55 males and 148 females, selected from English and French language and literature students of Tabriz University. Data were gathered through Procrastination Assessment Scale-student (PASS) and Causal Dimension Scale (CDA) and were analyzed by multiple regression analysis (stepwise). The results showed that there was a meaningful and negative relation between the locus of control and controllability in success context and academic procrastination. Besides, a meaningful and positive relation was observed between the locus of control and stability in failure context and procrastination. It was also found that 17% of the variance of procrastination was accounted by linear combination of attributions. We believe that causal attribution is a key in understanding procrastination in academic settings and is used by those who have the knowledge of Causal Attribution styles to organize their learning.
New Evidence on Causal Relationship between Approximate Number System (ANS) Acuity and Arithmetic Ability in Elementary-School Students: A Longitudinal Cross-Lagged Analysis.

PubMed

He, Yunfeng; Zhou, Xinlin; Shi, Dexin; Song, Hairong; Zhang, Hui; Shi, Jiannong

2016-01-01

Approximate number system (ANS) acuity and mathematical ability have been found to be closely associated in recent studies. However, whether and how these two measures are causally related still remain less addressed. There are two hypotheses about the possible causal relationship: ANS acuity influences mathematical performances, or access to math education sharpens ANS acuity. Evidences in support of both hypotheses have been reported, but these two hypotheses have never been tested simultaneously. Therefore, questions still remain whether only one-direction or reciprocal causal relationships existed in the association. In this work, we provided a new evidence on the causal relationship between ANS acuity and arithmetic ability. ANS acuity and mathematical ability of elementary-school students were measured sequentially at three time points within one year, and all possible causal directions were evaluated simultaneously using cross-lagged regression analysis. The results show that ANS acuity influences later arithmetic ability while the reverse causal direction was not supported. Our finding adds a strong evidence to the causal association between ANS acuity and mathematical ability, and also has important implications for educational intervention designed to train ANS acuity and thereby promote mathematical ability.
New Evidence on Causal Relationship between Approximate Number System (ANS) Acuity and Arithmetic Ability in Elementary-School Students: A Longitudinal Cross-Lagged Analysis

PubMed Central

He, Yunfeng; Zhou, Xinlin; Shi, Dexin; Song, Hairong; Zhang, Hui; Shi, Jiannong

2016-01-01

Approximate number system (ANS) acuity and mathematical ability have been found to be closely associated in recent studies. However, whether and how these two measures are causally related still remain less addressed. There are two hypotheses about the possible causal relationship: ANS acuity influences mathematical performances, or access to math education sharpens ANS acuity. Evidences in support of both hypotheses have been reported, but these two hypotheses have never been tested simultaneously. Therefore, questions still remain whether only one-direction or reciprocal causal relationships existed in the association. In this work, we provided a new evidence on the causal relationship between ANS acuity and arithmetic ability. ANS acuity and mathematical ability of elementary-school students were measured sequentially at three time points within one year, and all possible causal directions were evaluated simultaneously using cross-lagged regression analysis. The results show that ANS acuity influences later arithmetic ability while the reverse causal direction was not supported. Our finding adds a strong evidence to the causal association between ANS acuity and mathematical ability, and also has important implications for educational intervention designed to train ANS acuity and thereby promote mathematical ability. PMID:27462291
Optimal quantum networks and one-shot entropies

NASA Astrophysics Data System (ADS)

Chiribella, Giulio; Ebler, Daniel

2016-09-01

We develop a semidefinite programming method for the optimization of quantum networks, including both causal networks and networks with indefinite causal structure. Our method applies to a broad class of performance measures, defined operationally in terms of interative tests set up by a verifier. We show that the optimal performance is equal to a max relative entropy, which quantifies the informativeness of the test. Building on this result, we extend the notion of conditional min-entropy from quantum states to quantum causal networks. The optimization method is illustrated in a number of applications, including the inversion, charge conjugation, and controlization of an unknown unitary dynamics. In the non-causal setting, we show a proof-of-principle application to the maximization of the winning probability in a non-causal quantum game.
Implications of causality for quantum biology - I: topology change

NASA Astrophysics Data System (ADS)

Scofield, D. F.; Collins, T. C.

2018-06-01

A framework for describing the causal, topology changing, evolution of interacting biomolecules is developed. The quantum dynamical manifold equations (QDMEs) derived from this framework can be related to the causality restrictions implied by a finite speed of light and to Planck's constant to set a transition frequency scale. The QDMEs imply conserved stress-energy, angular-momentum and Noether currents. The functional whose extremisation leads to this result provides a causal, time-dependent, non-equilibrium generalisation of the Hohenberg-Kohn theorem. The system of dynamical equations derived from this functional and the currents J derived from the QDMEs are shown to be causal and consistent with the first and second laws of thermodynamics. This has the potential of allowing living systems to be quantum mechanically distinguished from non-living ones.
Causal uncertainty, claimed and behavioural self-handicapping.

PubMed

Thompson, Ted; Hepburn, Jonathan

2003-06-01

Causal uncertainty beliefs involve doubts about the causes of events, and arise as a consequence of non-contingent evaluative feedback: feedback that leaves the individual uncertain about the causes of his or her achievement outcomes. Individuals high in causal uncertainty are frequently unable to confidently attribute their achievement outcomes, experience anxiety in achievement situations and as a consequence are likely to engage in self-handicapping behaviour. Accordingly, we sought to establish links between trait causal uncertainty, claimed and behavioural self-handicapping. Participants were N=72 undergraduate students divided equally between high and low causally uncertain groups. We used a 2 (causal uncertainty status: high, low) x 3 (performance feedback condition: success, non-contingent success, non-contingent failure) between-subjects factorial design to examine the effects of causal uncertainty on achievement behaviour. Following performance feedback, participants completed 20 single-solution anagrams and 12 remote associate tasks serving as performance measures, and 16 unicursal tasks to assess practice effort. Participants also completed measures of claimed handicaps, state anxiety and attributions. Relative to low causally uncertain participants, high causally uncertain participants claimed more handicaps prior to performance on the anagrams and remote associates, reported higher anxiety, attributed their failure to internal, stable factors, and reduced practice effort on the unicursal tasks, evident in fewer unicursal tasks solved. These findings confirm links between trait causal uncertainty and claimed and behavioural self-handicapping, highlighting the need for educators to facilitate means by which students can achieve surety in the manner in which they attribute the causes of their achievement outcomes.
A review of covariate selection for non-experimental comparative effectiveness research.

PubMed

Sauer, Brian C; Brookhart, M Alan; Roy, Jason; VanderWeele, Tyler

2013-11-01

This paper addresses strategies for selecting variables for adjustment in non-experimental comparative effectiveness research and uses causal graphs to illustrate the causal network that relates treatment to outcome. Variables in the causal network take on multiple structural forms. Adjustment for a common cause pathway between treatment and outcome can remove confounding, whereas adjustment for other structural types may increase bias. For this reason, variable selection would ideally be based on an understanding of the causal network; however, the true causal network is rarely known. Therefore, we describe more practical variable selection approaches based on background knowledge when the causal structure is only partially known. These approaches include adjustment for all observed pretreatment variables thought to have some connection to the outcome, all known risk factors for the outcome, and all direct causes of the treatment or the outcome. Empirical approaches, such as forward and backward selection and automatic high-dimensional proxy adjustment, are also discussed. As there is a continuum between knowing and not knowing the causal, structural relations of variables, we recommend addressing variable selection in a practical way that involves a combination of background knowledge and empirical selection and that uses high-dimensional approaches. This empirical approach can be used to select from a set of a priori variables based on the researcher's knowledge to be included in the final analysis or to identify additional variables for consideration. This more limited use of empirically derived variables may reduce confounding while simultaneously reducing the risk of including variables that may increase bias. Copyright © 2013 John Wiley & Sons, Ltd.

A Review of Covariate Selection for Nonexperimental Comparative Effectiveness Research

PubMed Central

Sauer, Brian C.; Brookhart, Alan; Roy, Jason; Vanderweele, Tyler

2014-01-01

This paper addresses strategies for selecting variables for adjustment in non-experimental comparative effectiveness research (CER), and uses causal graphs to illustrate the causal network that relates treatment to outcome. Variables in the causal network take on multiple structural forms. Adjustment for on a common cause pathway between treatment and outcome can remove confounding, while adjustment for other structural types may increase bias. For this reason variable selection would ideally be based on an understanding of the causal network; however, the true causal network is rarely know. Therefore, we describe more practical variable selection approaches based on background knowledge when the causal structure is only partially known. These approaches include adjustment for all observed pretreatment variables thought to have some connection to the outcome, all known risk factors for the outcome, and all direct causes of the treatment or the outcome. Empirical approaches, such as forward and backward selection and automatic high-dimensional proxy adjustment, are also discussed. As there is a continuum between knowing and not knowing the causal, structural relations of variables, we recommend addressing variable selection in a practical way that involves a combination of background knowledge and empirical selection and that uses the high-dimensional approaches. This empirical approach can be used to select from a set of a priori variables based on the researcher’s knowledge to be included in the final analysis or to identify additional variables for consideration. This more limited use of empirically-derived variables may reduce confounding while simultaneously reducing the risk of including variables that may increase bias. PMID:24006330
Children's beliefs about causes of childhood depression and ADHD: a study of stigmatization.

PubMed

Coleman, Daniel; Walker, Janet S; Lee, Junghee; Friesen, Barbara J; Squire, Peter N

2009-07-01

Children's causal attributions about childhood mental health problems were examined in a national sample for prevalence; relative stigmatization; variation by age, race and ethnicity, and gender; and self-report of a diagnosis of depression or attention-deficit hyperactivity disorder (ADHD). A national sample of 1,091 children were randomly assigned to read vignettes about a peer with depression, ADHD, or asthma and respond to an online survey. Causal attributions and social distance were assessed, and correlations were examined. Logistic regression models for each causal item tested main effects and interaction terms for conditions, demographic characteristics, and self-reported diagnosis. The beliefs that parenting, substance abuse, and low effort caused the condition were all strongly intercorrelated and were moderately correlated with social distance. The depression condition was the strongest predictor of endorsement of the most stigmatizing causal beliefs. Stigmatizing causal beliefs were evident for ADHD, but with more modest effects. Children who reported a diagnosis were more likely to endorse parenting and substance abuse as causes (attenuated for ADHD). Modest to moderate effects were found for variation in causal beliefs across ethnic groups. This study demonstrated a consistent presence of stigmatization in children's beliefs about the causes of childhood mental health problems. Low effort, parenting, and substance abuse together tapped a moralistic and blaming view of mental health problems. The results reinforce the need to address stigmatization of mental disorders and the relative stigmatization of different causal beliefs. The findings of variation by ethnicity and diagnosis can inform and target antistigmatization efforts.
An assessment of predominant causal factors of pilot deviations that contribute to runway incursions

NASA Astrophysics Data System (ADS)

Campbell, Denado M.

The aim of this study was to identify predominant causal factors of pilot deviations in runway incursions over a two-year period. Runway incursion reports were obtained from NASA's Aviation Safety Reporting System (ASRS), and a qualitative method was used by classifying and coding each report to a specific causal factor(s). The causal factors that were used were substantiated by research from the Aircraft Owner's and Pilot's Association that found that these causal factors were the most common in runway incursion incidents and accidents. An additional causal factor was also utilized to determine the significance of pilot training in relation to runway incursions. From the reports examined, it was found that miscommunication and situational awareness have the greatest impact on pilots and are most often the major causes of runway incursions. This data can be used to assist airports, airlines, and the FAA to understand trends in pilot deviations, and to find solutions for specific problem areas in runway incursion incidents.
Property transmission: an explanatory account of the role of similarity information in causal inference.

PubMed

White, Peter A

2009-09-01

Many kinds of common and easily observed causal relations exhibit property transmission, which is a tendency for the causal object to impose its own properties on the effect object. It is proposed that property transmission becomes a general and readily available hypothesis used to make interpretations and judgments about causal questions under conditions of uncertainty, in which property transmission functions as a heuristic. The property transmission hypothesis explains why and when similarity information is used in causal inference. It can account for magical contagion beliefs, some cases of illusory correlation, the correspondence bias, overestimation of cross-situational consistency in behavior, nonregressive tendencies in prediction, the belief that acts of will are causes of behavior, and a range of other phenomena. People learn that property transmission is often moderated by other factors, but under conditions of uncertainty in which the operation of relevant other factors is unknown, it tends to exhibit a pervasive influence on thinking about causality. (c) 2009 APA, all rights reserved.
Beliefs About the Causal Structure of the Self-Concept Determine Which Changes Disrupt Personal Identity.

PubMed

Chen, Stephanie Y; Urminsky, Oleg; Bartels, Daniel M

2016-10-01

Personal identity is an important determinant of behavior, yet how people mentally represent their self-concepts and their concepts of other people is not well understood. In the current studies, we examined the age-old question of what makes people who they are. We propose a novel approach to identity that suggests that the answer lies in people's beliefs about how the features of identity (e.g., memories, moral qualities, personality traits) are causally related to each other. We examined the impact of the causal centrality of a feature, a key determinant of the extent to which a feature defines a concept, on judgments of identity continuity. We found support for this approach in three experiments using both measured and manipulated causal centrality. For judgments both of one's self and of others, we found that some features are perceived to be more causally central than others and that changes in such causally central features are believed to be more disruptive to identity.
Multiple-input multiple-output causal strategies for gene selection.

PubMed

Bontempi, Gianluca; Haibe-Kains, Benjamin; Desmedt, Christine; Sotiriou, Christos; Quackenbush, John

2011-11-25

Traditional strategies for selecting variables in high dimensional classification problems aim to find sets of maximally relevant variables able to explain the target variations. If these techniques may be effective in generalization accuracy they often do not reveal direct causes. The latter is essentially related to the fact that high correlation (or relevance) does not imply causation. In this study, we show how to efficiently incorporate causal information into gene selection by moving from a single-input single-output to a multiple-input multiple-output setting. We show in synthetic case study that a better prioritization of causal variables can be obtained by considering a relevance score which incorporates a causal term. In addition we show, in a meta-analysis study of six publicly available breast cancer microarray datasets, that the improvement occurs also in terms of accuracy. The biological interpretation of the results confirms the potential of a causal approach to gene selection. Integrating causal information into gene selection algorithms is effective both in terms of prediction accuracy and biological interpretation.
Preschoolers’ Development of Theory of Mind: The Contribution of Understanding Psychological Causality in Stories

PubMed Central

Sanefuji, Wakako; Haryu, Etsuko

2018-01-01

This study investigated the relationship between children’s abilities to understand causal sequences and another’s false belief. In Experiment 1, we tested 3-, 4-, 5-, and 6-year-old children (n = 28, 28, 27, and 27, respectively) using false belief and picture sequencing tasks involving mechanical, behavioral, and psychological causality. Understanding causal sequences in mechanical, behavioral, and psychological stories was related to understanding other’s false beliefs. In Experiment 2, children who failed the initial false belief task (n = 50) were reassessed 5 months later. High scorers in the sequencing of the psychological stories in Experiment 1 were more likely to pass the standard false belief task than were the low scorers. Conversely, understanding causal sequences in the mechanical and behavioral stories in Experiment 1 did not predict passing the false belief task in Experiment 2. Thus, children may understand psychological causality before they are able to use it to understand false beliefs.
Causal inference in nonlinear systems: Granger causality versus time-delayed mutual information

NASA Astrophysics Data System (ADS)

Li, Songting; Xiao, Yanyang; Zhou, Douglas; Cai, David

2018-05-01

The Granger causality (GC) analysis has been extensively applied to infer causal interactions in dynamical systems arising from economy and finance, physics, bioinformatics, neuroscience, social science, and many other fields. In the presence of potential nonlinearity in these systems, the validity of the GC analysis in general is questionable. To illustrate this, here we first construct minimal nonlinear systems and show that the GC analysis fails to infer causal relations in these systems—it gives rise to all types of incorrect causal directions. In contrast, we show that the time-delayed mutual information (TDMI) analysis is able to successfully identify the direction of interactions underlying these nonlinear systems. We then apply both methods to neuroscience data collected from experiments and demonstrate that the TDMI analysis but not the GC analysis can identify the direction of interactions among neuronal signals. Our work exemplifies inference hazards in the GC analysis in nonlinear systems and suggests that the TDMI analysis can be an appropriate tool in such a case.
Causal learning with local computations.

PubMed

Fernbach, Philip M; Sloman, Steven A

2009-05-01

The authors proposed and tested a psychological theory of causal structure learning based on local computations. Local computations simplify complex learning problems via cues available on individual trials to update a single causal structure hypothesis. Structural inferences from local computations make minimal demands on memory, require relatively small amounts of data, and need not respect normative prescriptions as inferences that are principled locally may violate those principles when combined. Over a series of 3 experiments, the authors found (a) systematic inferences from small amounts of data; (b) systematic inference of extraneous causal links; (c) influence of data presentation order on inferences; and (d) error reduction through pretraining. Without pretraining, a model based on local computations fitted data better than a Bayesian structural inference model. The data suggest that local computations serve as a heuristic for learning causal structure. Copyright 2009 APA, all rights reserved.
Factors Relating to Staff Attributions of Control over Challenging Behaviour

ERIC Educational Resources Information Center

Dilworth, Jennifer A.; Phillips, Neil; Rose, John

2011-01-01

Background: Previous research has suggested that severity of intellectual disability (ID) and topography of behaviour may influence staff causal attributions regarding challenging behaviour. Subsequently, these causal attributions may influence helping behaviours. This study investigated the relationship between attributions of control over…
Discourse comprehension in L2: Making sense of what is not explicitly said.

PubMed

Foucart, Alice; Romero-Rivas, Carlos; Gort, Bernharda Lottie; Costa, Albert

2016-12-01

Using ERPs, we tested whether L2 speakers can integrate multiple sources of information (e.g., semantic, pragmatic information) during discourse comprehension. We presented native speakers and L2 speakers with three-sentence scenarios in which the final sentence was highly causally related, intermediately related, or causally unrelated to its context; its interpretation therefore required simple or complex inferences. Native speakers revealed a gradual N400-like effect, larger in the causally unrelated condition than in the highly related condition, and falling in-between in the intermediately related condition, replicating previous results. In the crucial intermediately related condition, L2 speakers behaved like native speakers, however, showing extra processing in a later time-window. Overall, the results show that, when reading, L2 speakers are able to process information from the local context and prior information (e.g., world knowledge) to build global coherence, suggesting that they process different sources of information to make inferences online during discourse comprehension, like native speakers. Copyright © 2016 Elsevier Inc. All rights reserved.
Spatio-temporal Granger causality: a new framework

PubMed Central

Luo, Qiang; Lu, Wenlian; Cheng, Wei; Valdes-Sosa, Pedro A.; Wen, Xiaotong; Ding, Mingzhou; Feng, Jianfeng

2015-01-01

That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framework, Granger causality is redefined as a global index measuring the directed information flow between two time series with time-varying properties. Both theoretical analyses and numerical examples demonstrate that Granger causality is a monotonically increasing function of the temporal resolution used in the estimation. This is consistent with the general principle of coarse graining, which causes information loss by smoothing out very fine-scale details in time and space. Our results confirm that the Granger causality at the finer spatio-temporal scales considerably outperforms the traditional approach in terms of an improved consistency between two resting-state scans of the same subject. To optimally estimate the Granger causality, the proposed theoretical framework is implemented through a combination of several approaches, such as dividing the optimal time window and estimating the parameters at the fine temporal and spatial scales. Taken together, our approach provides a novel and robust framework for estimating the Granger causality from fMRI, EEG, and other related data. PMID:23643924
Causal beliefs about intellectual disability and schizophrenia and their relationship with awareness of the condition and social distance.

PubMed

Scior, Katrina; Furnham, Adrian

2016-09-30

Evidence on mental illness stigma abounds yet little is known about public perceptions of intellectual disability. This study examined causal beliefs about intellectual disability and schizophrenia and how these relate to awareness of the condition and social distance. UK lay people aged 16+(N=1752), in response to vignettes depicting intellectual disability and schizophrenia, noted their interpretation of the difficulties, and rated their agreement with 22 causal and four social distance items. They were most likely to endorse environmental causes for intellectual disability, and biomedical factors, trauma and early disadvantage for schizophrenia. Accurate identification of both vignettes was associated with stronger endorsement of biomedical causes, alongside weaker endorsement of adversity, environmental and supernatural causes. Biomedical causal beliefs and social distance were negatively correlated for intellectual disability, but not for schizophrenia. Causal beliefs mediated the relationship between identification of the condition and social distance for both conditions. While all four types of causal beliefs acted as mediators for intellectual disability, for schizophrenia only supernatural causal beliefs did. Educating the public and promoting certain causal beliefs may be of benefit in tackling intellectual disability stigma, but for schizophrenia, other than tackling supernatural attributions, may be of little benefit in reducing stigma. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Functional brain networks and white matter underlying theory-of-mind in autism.

PubMed

Kana, Rajesh K; Libero, Lauren E; Hu, Christi P; Deshpande, Hrishikesh D; Colburn, Jeffrey S

2014-01-01

Human beings constantly engage in attributing causal explanations to one's own and to others' actions, and theory-of-mind (ToM) is critical in making such inferences. Although children learn causal attribution early in development, children with autism spectrum disorders (ASDs) are known to have impairments in the development of intentional causality. This functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) study investigated the neural correlates of physical and intentional causal attribution in people with ASDs. In the fMRI scanner, 15 adolescents and adults with ASDs and 15 age- and IQ-matched typically developing peers made causal judgments about comic strips presented randomly in an event-related design. All participants showed robust activation in bilateral posterior superior temporal sulcus at the temporo-parietal junction (TPJ) in response to intentional causality. Participants with ASDs showed lower activation in TPJ, right inferior frontal gyrus and left premotor cortex. Significantly weaker functional connectivity was also found in the ASD group between TPJ and motor areas during intentional causality. DTI data revealed significantly reduced fractional anisotropy in ASD participants in white matter underlying the temporal lobe. In addition to underscoring the role of TPJ in ToM, this study found an interaction between motor simulation and mentalizing systems in intentional causal attribution and its possible discord in autism.
Gut Microbiota and Energy Expenditure in Health and Obesity.

PubMed

Bakker, Guido J; Zhao, Jing; Herrema, Hilde; Nieuwdorp, Max

2015-01-01

The contribution of intestinal bacterial strains (gut microbiota) to the development of obesity and obesity-related disorders is increasingly recognized as a potential diagnostic and pharmacologic target. Alterations in the intestinal bacterial composition have been associated with presence of chronic low-grade inflammation, a known feature of insulin resistance and type 2 diabetes mellitus. However, causality still needs to be proven. Fecal transplantation studies in germ-free mice have provided crucial insight into the causality of gut microbiota in development of obesity and obesity-related disorders. Moreover, fecal transplantation studies in conjunction with fecal sampling in prospectively followed cohorts will help identify causally involved intestinal bacterial strains in human obesity. Results from these studies will lead to characterization of novel diagnostic markers as well as therapeutic strategies that aim to treat obesity and obesity-related disorders.
The Impact of Relative Poverty on Norwegian Adolescents’ Subjective Health: A Causal Analysis with Propensity Score Matching

PubMed Central

Elstad, Jon Ivar; Pedersen, Axel West

2012-01-01

Studies have revealed that relative poverty is associated with ill health, but the interpretations of this correlation vary. This article asks whether relative poverty among Norwegian adolescents is causally related to poor subjective health, i.e., self-reported somatic and mental symptoms. Data consist of interview responses from a sample of adolescents (N = 510) and their parents, combined with register data on the family’s economic situation. Relatively poor adolescents had significantly worse subjective health than non-poor adolescents. Relatively poor adolescents also experienced many other social disadvantages, such as parental unemployment and parental ill health. Comparisons between the relatively poor and the non-poor adolescents, using propensity score matching, indicated a negative impact of relative poverty on the subjective health among those adolescents who lived in families with relatively few economic resources. The results suggest that there is a causal component in the association between relative poverty and the symptom burden of disadvantaged adolescents. Relative poverty is only one of many determinants of adolescents’ subjective health, but its role should be acknowledged when policies for promoting adolescent health are designed. PMID:23249858
Income and obesity: what is the direction of the relationship? A systematic review and meta-analysis

PubMed Central

Kim, Tae Jun; von dem Knesebeck, Olaf

2018-01-01

Objective It was repeatedly shown that lower income is associated with higher risks for subsequent obesity. However, the perspective of a potential reverse causality is often neglected, in which obesity is considered a cause for lower income, when obese people drift into lower-income jobs due to labour–market discrimination and public stigmatisation. This review was performed to explore the direction of the relation between income and obesity by specifically assessing the importance of social causation and reverse causality. Design Systematic review and meta-analysis. Methods A systematic literature search was conducted in January 2017. The databases Medline, PsycINFO, Sociological Abstracts, International Bibliography of Social Sciences and Sociological Index were screened to identify prospective cohort studies with quantitative data on the relation between income and obesity. Meta-analytic methods were applied using random-effect models, and the quality of studies assessed with the Newcastle-Ottawa Scale. Results In total, 21 studies were eligible for meta-analysis. All included studies originated from either the USA (n=16), the UK (n=3) or Canada (n=2). From these, 14 studies on causation and 7 studies on reverse causality were found. Meta-analyses revealed that lower income is associated with subsequent obesity (OR 1.27, 95% CI 1.10 to 1.47; risk ratio 1.52, 95% CI 1.08 to 2.13), though the statistical significance vanished once adjusted for publication bias. Studies on reverse causality indicated a more consistent relation between obesity and subsequent income, even after taking publication bias into account (standardised mean difference −0.15, 95% CI −0.30 to 0.01). Sensitivity analyses implied that the association is influenced by obesity measurement, gender, length of observation and study quality. Conclusions Findings suggest that there is more consistent evidence for reverse causality. Therefore, there is a need to examine reverse causality processes in more detail to understand the relation between income and obesity. PROSPERO registration number 42016041296. PMID:29306894
Causal interpretation of correlational studies – Analysis of medical news on the website of the official journal for German physicians

PubMed Central

Rahn, Anne Christin; Bock, Merle; Mühlhauser, Ingrid

2018-01-01

Background Media frequently draws inappropriate causal statements from observational studies. We analyzed the reporting of study results in the Medical News section of the German medical journal Deutsches Ärzteblatt (DÄ). Methods Study design: Retrospective quantitative content analysis of randomly selected news reports and related original journal articles and press releases. A medical news report was selected if headlines comprised at least two linked variables. Two raters independently categorized the headline and text of each news report, conclusions of the abstract and full text of the related journal article, and the press release. The assessment instrument comprised five categories from ‘neutral’ to ‘unconditionally causal’. Outcome measures: degree of matching between 1) news headlines and conclusions of the journal article, 2) headlines and text of news reports, 3) text and conclusions, and 4) headlines and press releases. We analyzed whether news headlines rated as unconditionally causal based on randomized controlled trials (RCTs). Results One-thousand eighty-seven medical news reports were published between April 2015 and May 2016. The final random sample comprised 176 news reports and 100 related press releases. Degree of matching: 1) 45% (79/176) for news headlines and journal article conclusions, 2) 55% (97/176) for headlines and text, 3) 53% (93/176) for text and conclusions, and 4) 41% (41/100) for headlines and press releases. Exaggerations were found in 45% (80/176) of the headlines compared to the conclusions of the related journal article. Sixty-five of 137 unconditionally causal statements of the news headlines were phrased more weakly in the subsequent news text body. Only 52 of 137 headlines (38%) categorized as unconditionally causal reported RCTs. Conclusion Reporting of medical news in the DÄ medical journal is misleading. Most headlines that imply causal associations were not based on RCTs. Medical journalists should follow standards of reporting scientific study results. PMID:29723258
Causality from the Cosmological Perspective in Vedanta and Western Physics.

NASA Astrophysics Data System (ADS)

Hawley, Danny Lee

The relation between Western physics and Indian Vedanta philosophy is investigated through the topic of causality, taken in the sense of explanatory theories of the origin of the universe and the relations among its physical, mental, and spiritual aspects. Both physics and Vedanta have a common goal of explanation by means of a unitary principle. While physics has long been separated from metaphysics, its discoveries indicate that consciousness must be included in a complete explanation. Consciousness is taken as the fundamental basis and source of all phenomena in Vedanta. This work traces the developments of causal explanation in Western physics and Indian philosophy, and considers how these views may relate to each other and how they may together suggest a comprehensive view of reality. Approaches typically applied by historians of religion to the study of creation myths, especially the psychological approach which considers myths from the perspective or cyclical stages of conscious development, are applied to the causal theories of the two cultures. The question of how causal explanations attempt to bridge the gap between cause and effect, unity and multiplicity, absolute and relative, conscious and unconscious, etc., is addressed. Though the investigation begins from the earliest causal explanations, viz., creation myths, emphasis is placed upon Samkara's commentaries of Advaita Vedanta, examined in the original Sanskrit, and upon the convergence of modern field theory, astrophysics, and cosmology, seen from the perspective of a previous doctorate in physics. Consideration is given to the comparison between physics and Vedanta as to goals, methods, and domains, to the question of the incompleteness of physics and the extent to which it nevertheless points beyond itself, to the possibility of a synthetic view and how it might be effected, and to analogies and metaphors through which physics and Vedanta may illuminate each other. An intuitive picture is presented which uses an analogy between higher dimensions and levels of consciousness to suggest a comprehensive view.
SU-E-P-07: Retrospective Analysis of Incident Reports at a Radiology Department: Feedback From Incident Reporting System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kakinohana, Y; Toita, T; Heianna, J

Purpose: To provide an overview of reported incidents that occurred in a radiology department and to describe the most common causal source of incidents. Methods: Incident reports from the radiology department at the University of the Ryukyus Hospital between 2008 and 2013 were collected and analyzed retrospectively. The incident report form contains the following items, causal factors of the incident and desirable corrective actions to prevent recurrence of similar incidents. These items allow the institution to investigate/analyze root causes of the incidents and suggest measures to be taken to prevent further, similar incidents. The ‘causal factors of the incident’ itemmore » comprises multiple selections from among 24 selections and includes some synonymous selections. In this study, this item was re-categorized into four causal source types: (i) carelessness, (ii) lack of skill or knowledge, (iii) deficiencies in communication, and (iv) external factors. Results: There were a total of 7490 incident reports over the study period and 276 (3.7%) were identified as originating from the radiology department. The most frequent causal source type was carelessness (62%). The other three types showed similar frequencies (10–14%). The staff members involved in incidents indicate three predominant desirable corrective actions to prevent or decrease the recurrence of similar incidents. These are ‘improvement in communication’ (24%), ‘staff training/education’ (19%), and ‘daily medical procedures’ (22%), and the most frequent was ‘improvement in communication’. Even though the most frequent causal factor was related to carelessness, the most desirable corrective action indicated by the staff members was related to communication. Conclusion: Our finding suggests that the most immediate causes are strongly related to carelessness. However, the most likely underlying causes of incidents would be related to deficiencies in effective communication. At our department, therefore, the primary action to prevent/reduce similar incidents should be ‘communication improvement’.« less

Nonlocal character of quantum theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stapp, H.P.

1997-04-01

According to a common conception of causality, the truth of a statement that refers only to phenomena confined to an earlier time cannot depend upon which measurement an experimenter will freely choose to perform at a later time. According to a common idea of the theory of relativity this causality condition should be valid in all Lorentz frames. It is shown here that this concept of relativistic causality is incompatible with some simple predictions of quantum theory. {copyright} {ital 1997 American Association of Physics Teachers.}
Non-Separability and Synchronicity: Pauli, Jung and a New Historical, Philosophical Perspective on Quantum Physics

NASA Astrophysics Data System (ADS)

Giannetto, E. A.; Pozzi, F.

We would like to discuss the historical emergence of quantum physics and quantum non-separability, by analysing Pauli's point of view in relation to Jung's ideas. Recent inquiries on EPR shows that quantum non-separability indicates an a-causal connection of the "quantum reality" for space-like intervals ("simultaneity region ") of world (measurement) events: this non-causal connection is the physical counterpart of what Jung called "synchronicity " with an assessment given also by Pauli. This does not imply any violation of mechanical causality by any introduction of action-at-a-distance. From a physical point of view a-causal connections can be interpreted as implying a particular quantum topology of space-time, which leads to a non-mechanistic conception of nature and which could be related to a holistic quantum dynamical reality of the world like Bohm's "holomovement" or "light". This kind of non-mechanistic conception of nature as well as the idea of non-separability of the world and of synchronicity, as stated by Jung itself, was developed by Leibnitz: from this point of view, we can look at quantum physics (as well as for relativity it was shown) as related to a new emergence of concepts belonging to the Leibnitzian (anti-Newtonian) tradition.
Causal fermion systems as a candidate for a unified physical theory

NASA Astrophysics Data System (ADS)

Finster, Felix; Kleiner, Johannes

2015-07-01

The theory of causal fermion systems is an approach to describe fundamental physics. Giving quantum mechanics, general relativity and quantum field theory as limiting cases, it is a candidate for a unified physical theory. We here give a non-technical introduction.
Academic Procrastination: The Relationship Between Causal Attribution Styles and Behavioral Postponement

PubMed Central

Badri Gargari, Rahim; Sabouri, Hossein; Norzad, Fatemeh

2011-01-01

Objective: This research was conducted to study the relationship between attribution and academic procrastination in University Students. Methods: The subjects were 203 undergraduate students, 55 males and 148 females, selected from English and French language and literature students of Tabriz University. Data were gathered through Procrastination Assessment Scale-student (PASS) and Causal Dimension Scale (CDA) and were analyzed by multiple regression analysis (stepwise). Results: The results showed that there was a meaningful and negative relation between the locus of control and controllability in success context and academic procrastination. Besides, a meaningful and positive relation was observed between the locus of control and stability in failure context and procrastination. It was also found that 17% of the variance of procrastination was accounted by linear combination of attributions. Conclusion: We believe that causal attribution is a key in understanding procrastination in academic settings and is used by those who have the knowledge of Causal Attribution styles to organize their learning. PMID:24644450
Diagnostic nomenclature for foetal alcohol spectrum disorders: the continuing challenge of causality.

PubMed

Miller, A R

2013-11-01

Prenatal alcohol exposure is a risk factor for neurologically based cognitive and adaptive disability. Diagnostic nomenclature for prenatally exposed children with cognitive and adaptive disability who lack features for foetal alcohol syndrome (FAS) or partial FAS includes the terms alcohol-related neurodevelopmental disorder (ARND) and foetal alcohol spectrum disorder(s) (FASD). Although these terms are now widely used, this paper argues that both are problematic. ARND is flawed by unjustifiably turning a risk factor into a causal factor and shrouding the result in terminological ambiguity, while FASD is not appropriate as a clinical label, and its use as a proxy for ARND deflects critical attention from the causal inferencing that is integral to diagnosing children with an alcohol-related teratogenic condition. Existing nomenclature is at odds with logical and evidence-based diagnosing and also has implications for interpretation of epidemiological data. Diagnostic nomenclature that is not tightly linked to causal inference is preferable at the present stage of this field's development. © 2013 John Wiley & Sons Ltd.
Attention to irrelevant cues is related to positive symptoms in schizophrenia.

PubMed

Morris, Richard; Griffiths, Oren; Le Pelley, Michael E; Weickert, Thomas W

2013-05-01

Many modern learning theories assume that the amount of attention to a cue depends on how well that cue predicted important events in the past. Schizophrenia is associated with deficits in attention and recent theories of psychosis have argued that positive symptoms such as delusions and hallucinations are related to a failure of selective attention. However, evidence demonstrating that attention to irrelevant cues is related to positive symptoms in schizophrenia is lacking. We used a novel method of measuring attention to nonpredictive (and thus irrelevant) cues in a causal learning test (Le Pelley ME, McLaren IP. Learned associability and associative change in human causal learning. Q J Exp Psychol B. 2003;56:68-79) to assess whether healthy adults and people with schizophrenia discriminate previously predictive and nonpredictive cues. In a series of experiments with independent samples, we demonstrated: (1) when people with schizophrenia who had severe positive symptoms successfully distinguished between predictive and nonpredictive cues during training, they failed to discriminate between predictive and nonpredictive cues relative to healthy adults during subsequent testing and (2) learning about nonpredictive cues was correlated with more severe positive symptoms scores in schizophrenia. These results suggest that positive symptoms of schizophrenia are related to increased attention to nonpredictive cues during causal learning. This deficit in selective attention results in learning irrelevant causal associations and may be the basis of positive symptoms in schizophrenia.
Econometric testing on linear and nonlinear dynamic relation between stock prices and macroeconomy in China

NASA Astrophysics Data System (ADS)

Borjigin, Sumuya; Yang, Yating; Yang, Xiaoguang; Sun, Leilei

2018-03-01

Many researchers have realized that there is a strong correlation between stock prices and macroeconomy. In order to make this relationship clear, a lot of studies have been done. However, the causal relationship between stock prices and macroeconomy has still not been well explained. A key point is that, most of the existing research adopts linear and stable models to investigate the correlation of stock prices and macroeconomy, while the real causality of that may be nonlinear and dynamic. To fill this research gap, we investigate the nonlinear and dynamic causal relationships between stock prices and macroeconomy. Based on the case of China's stock prices and acroeconomy measures from January 1992 to March 2017, we compare the linear Granger causality test models with nonlinear ones. Results demonstrate that the nonlinear dynamic Granger causality is much stronger than linear Granger causality. From the perspective of nonlinear dynamic Granger causality, China's stock prices can be viewed as "national economic barometer". On the one hand, this study will encourage researchers to take nonlinearity and dynamics into account when they investigate the correlation of stock prices and macroeconomy; on the other hand, our research can guide regulators and investors to make better decisions.
Causal-explanatory pluralism: How intentions, functions, and mechanisms influence causal ascriptions.

PubMed

Lombrozo, Tania

2010-12-01

Both philosophers and psychologists have argued for the existence of distinct kinds of explanations, including teleological explanations that cite functions or goals, and mechanistic explanations that cite causal mechanisms. Theories of causation, in contrast, have generally been unitary, with dominant theories focusing either on counterfactual dependence or on physical connections. This paper argues that both approaches to causation are psychologically real, with different modes of explanation promoting judgments more or less consistent with each approach. Two sets of experiments isolate the contributions of counterfactual dependence and physical connections in causal ascriptions involving events with people, artifacts, or biological traits, and manipulate whether the events are construed teleologically or mechanistically. The findings suggest that when events are construed teleologically, causal ascriptions are sensitive to counterfactual dependence and relatively insensitive to the presence of physical connections, but when events are construed mechanistically, causal ascriptions are sensitive to both counterfactual dependence and physical connections. The conclusion introduces an account of causation, an "exportable dependence theory," that provides a way to understand the contributions of physical connections and teleology in terms of the functions of causal ascriptions. Copyright © 2010 Elsevier Inc. All rights reserved.
Individuals with currently untreated mental illness: causal beliefs and readiness to seek help.

PubMed

Stolzenburg, S; Freitag, S; Evans-Lacko, S; Speerforck, S; Schmidt, S; Schomerus, G

2018-01-16

Many people with mental illness do not seek professional help. Beliefs about the causes of their current health problem seem relevant for initiating treatment. Our aim was to find out to what extent the perceived causes of current untreated mental health problems determine whether a person considers herself/himself as having a mental illness, perceives need for professional help and plans to seek help in the near future. In a cross-sectional study, we examined 207 untreated persons with a depressive syndrome, all fulfilling criteria for a current mental illness as confirmed with a structured diagnostic interview (Mini International Neuropsychiatric Interview). The sample was recruited in the community using adverts, flyers and social media. We elicited causal explanations for the present problem, depression literacy, self-identification as having a mental illness, perceived need for professional help, help-seeking intentions, severity of depressive symptoms (Patient Health Questionnaire - Depression), and whether respondents had previously sought mental healthcare. Most participants fulfilled diagnostic criteria for a mood disorder (n = 181, 87.4%) and/or neurotic, stress-related and somatoform disorders (n = 120, 58.0%) according to the ICD-10. N = 94 (45.4%) participants had never received mental health treatment previously. Exploratory factor analysis of a list of 25 different causal explanations resulted in five factors: biomedical causes, person-related causes, childhood trauma, current stress and unhealthy behaviour. Attributing the present problem to biomedical causes, person-related causes, childhood trauma and stress were all associated with stronger self-identification as having a mental illness. In persons who had never received mental health treatment previously, attribution to biomedical causes was related to greater perceived need and stronger help-seeking intentions. In those with treatment experience, lower attribution to person-related causes and stress were related to greater perceived need for professional help. While several causal explanations are associated with self-identification as having a mental illness, only biomedical attributions seem to be related to increase perceived need and help-seeking intentions, especially in individuals with no treatment experiences. Longitudinal studies investigating causal beliefs and help-seeking are needed to find out how causal attributions guide help-seeking behaviour. From this study it seems possible that portraying professional mental health treatment as not being restricted to biomedical problems would contribute to closing the treatment gap for mental disorders.
Expanding the basic science debate: the role of physics knowledge in interpreting clinical findings.

PubMed

Goldszmidt, Mark; Minda, John Paul; Devantier, Sarah L; Skye, Aimee L; Woods, Nicole N

2012-10-01

Current research suggests a role for biomedical knowledge in learning and retaining concepts related to medical diagnosis. However, learning may be influenced by other, non-biomedical knowledge. We explored this idea using an experimental design and examined the effects of causal knowledge on the learning, retention, and interpretation of medical information. Participants studied a handout about several respiratory disorders and how to interpret respiratory exam findings. The control group received the information in standard "textbook" format and the experimental group was presented with the same information as well as a causal explanation about how sound travels through lungs in both the normal and disease states. Comprehension and memory of the information was evaluated with a multiple-choice exam. Several questions that were not related to the causal knowledge served as control items. Questions related to the interpretation of physical exam findings served as the critical test items. The experimental group outperformed the control group on the critical test items, and our study shows that a causal explanation can improve a student's memory for interpreting clinical details. We suggest an expansion of which basic sciences are considered fundamental to medical education.
Inferring interventional predictions from observational learning data.

PubMed

Meder, Bjorn; Hagmayer, York; Waldmann, Michael R

2008-02-01

Previous research has shown that people are capable of deriving correct predictions for previously unseen actions from passive observations of causal systems (Waldmann & Hagmayer, 2005). However, these studies were limited, since learning data were presented as tabulated data only, which may have turned the task more into a reasoning rather than a learning task. In two experiments, we therefore presented learners with trial-by-trial observational learning input referring to a complex causal model consisting of four events. To test the robustness of the capacity to derive correct observational and interventional inferences, we pitted causal order against the temporal order of learning events. The results show that people are, in principle, capable of deriving correct predictions after purely observational trial-by-trial learning, even with relatively complex causal models. However, conflicting temporal information can impair performance, particularly when the inferences require taking alternative causal pathways into account.
[Causal inference in medicine--a historical view in epidemiology].

PubMed

Tsuda, T; Babazono, A; Mino, Y; Matsuoka, H; Yamamoto, E

1996-07-01

Changes of causal inference concepts in medicine, especially those having to do with chronic diseases, were reviewed. The review is divided into five sections. First, several articles on the increased academic acceptance of observational research are cited. Second, the definitions of confounder and effect modifier concepts are explained. Third, the debate over the so-called "criteria for causal inference" was discussed. Many articles have pointed out various problems related to the lack of logical bases for standard criteria, however, such criteria continue to be misapplied in Japan. Fourth, the Popperian and verificationist concepts of causal inference are summarized. Lastly, a recent controversy on meta-analysis is explained. Causal inference plays an important role in epidemiologic theory and medicine. However, because this concept has not been well-introduced in Japan, there has been much misuse of the concept, especially when used for conventional criteria.
Granger Causality Testing with Intensive Longitudinal Data.

PubMed

Molenaar, Peter C M

2018-06-01

The availability of intensive longitudinal data obtained by means of ambulatory assessment opens up new prospects for prevention research in that it allows the derivation of subject-specific dynamic networks of interacting variables by means of vector autoregressive (VAR) modeling. The dynamic networks thus obtained can be subjected to Granger causality testing in order to identify causal relations among the observed time-dependent variables. VARs have two equivalent representations: standard and structural. Results obtained with Granger causality testing depend upon which representation is chosen, yet no criteria exist on which this important choice can be based. A new equivalent representation is introduced called hybrid VARs with which the best representation can be chosen in a data-driven way. Partial directed coherence, a frequency-domain statistic for Granger causality testing, is shown to perform optimally when based on hybrid VARs. An application to real data is provided.
Quantum violation of an instrumental test

NASA Astrophysics Data System (ADS)

Chaves, Rafael; Carvacho, Gonzalo; Agresti, Iris; Di Giulio, Valerio; Aolita, Leandro; Giacomini, Sandro; Sciarrino, Fabio

2018-03-01

Inferring causal relations from experimental observations is of primal importance in science. Instrumental tests provide an essential tool for that aim, as they allow one to estimate causal dependencies even in the presence of unobserved common causes. In view of Bell's theorem, which implies that quantum mechanics is incompatible with our most basic notions of causality, it is of utmost importance to understand whether and how paradigmatic causal tools obtained in a classical setting can be carried over to the quantum realm. Here we show that quantum effects imply radically different predictions in the instrumental scenario. Among other results, we show that an instrumental test can be violated by entangled quantum states. Furthermore, we demonstrate such violation using a photonic set-up with active feed-forward of information, thus providing an experimental proof of this new form of non-classical behaviour. Our findings have fundamental implications in causal inference and may also lead to new applications of quantum technologies.
Sex differences in the inference and perception of causal relations within a video game

PubMed Central

Young, Michael E.

2014-01-01

The learning of immediate causation within a dynamic environment was examined. Participants encountered seven decision points in which they needed to choose, which of three possible candidates was the cause of explosions in the environment. Each candidate was firing a weapon at random every few seconds, but only one of them produced an immediate effect. Some participants showed little learning, but most demonstrated increases in accuracy across time. On average, men showed higher accuracy and shorter latencies that were not explained by differences in self-reported prior video game experience. This result suggests that prior reports of sex differences in causal choice in the game are not specific to situations involving delayed or probabilistic causal relations. PMID:25202293
Sex differences in the inference and perception of causal relations within a video game.

PubMed

Young, Michael E

2014-01-01

The learning of immediate causation within a dynamic environment was examined. Participants encountered seven decision points in which they needed to choose, which of three possible candidates was the cause of explosions in the environment. Each candidate was firing a weapon at random every few seconds, but only one of them produced an immediate effect. Some participants showed little learning, but most demonstrated increases in accuracy across time. On average, men showed higher accuracy and shorter latencies that were not explained by differences in self-reported prior video game experience. This result suggests that prior reports of sex differences in causal choice in the game are not specific to situations involving delayed or probabilistic causal relations.
DES daughters in France: experts' points of view on the various genital, uterine and obstetric pathologies, and in utero DES exposure.

PubMed

Clement, R; Guilbaud, E; Barrios, L; Rougé-Maillart, C; Jousset, N; Rodat, O

2014-10-01

Compensation of diethylstilbestrol exposure depends on the judicial system. In France, girls having been exposed to diethylstilbestrol are currently being compensated, and each exposure victim is being evaluated. Fifty-nine expert evaluations were studied to determine the causal relation between exposure to diethylstilbestrol and the pathologies attributable to diethylstilbestrol. The following were taken into consideration: age at the first signs of the pathology; age of the sufferer at the time of evaluation; the pathologies grouped into five categories: fertility disorders - cancers - mishaps during pregnancy - psychosomatic complaints - pathologies of "3rd generation DES victims"; submission of proof of DES exposure; the degree of causality determined (direct, indirect, ruled out). 61% of the cases related to fertility disorders, 28.8% to cancer pathologies (clear-cell adenocarcinoma), 18.6% to mishaps during pregnancy, 8.5% to disorders resulting from preterm delivery, and 3.4% to psychosomatic disorders. Some cases involved a combination of two types of complaints. Indirect causality was determined in 47.1% of the cases involving primary sterility, in 66.7% involving secondary sterility, and in 5 out of 6 cases of total sterility. There is direct causality between in utero diethylstilbestrol exposure and vaginal or cervical clear cell adenocarcinoma. Causality is indirect in the case of disorders linked to prematurity in third generation victims. Causality was determined by the experts on the basis of scientific criteria which attribute the presenting pathologies to diethylstilbestrol exposure. When other risk factors come into play, or when exposure is indirect (third generation), this causality is diminished. © IMechE 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.
Causal Attribution and Coping Maxims Differences between Immigrants and Non-Immigrants Suffering from Back Pain in Switzerland.

PubMed

Mantwill, Sarah; Schulz, Peter J

2016-01-01

This study aimed at investigating the relationship between causal attributions and coping maxims in people suffering from back pain. Further, it aimed at identifying in how far causal attributions and related coping maxims would defer between immigrants and non-immigrants in Switzerland. Data for this study came from a larger survey study that was conducted among immigrant populations in the German- and Italian-speaking part of Switzerland. Included in the analyses were native Swiss participants, as well as Albanian- and Serbian-speaking immigrants, who had indicated to have suffered from back pain within the last 12 months prior to the study. Data was analyzed for overall 495 participants. Items for causal attributions and coping maxims were subject to factor analyses. Cultural differences were assessed with ANOVA and regression analyses. Interaction terms were included to investigate whether the relationship between causal attributions and coping maxims would differ with cultural affiliation. For both immigrant groups the physician's influence on the course of their back pain was more important than for Swiss participants (p <.05). With regard to coping, both immigrant groups were more likely to agree with maxims that were related to the improvement of the back pain, as well as the acceptance of the current situation (p <.05). The only consistent interaction effect that was found indicated that being Albanian-speaking negatively moderated the relationship between physical activity as an attributed cause of back pain and all three identified coping maxims. The study shows that differences in causal attribution and coping maxims between immigrants and non-immigrants exist. Further, the results support the assumption of an association between causal attribution and coping maxims. However cultural affiliation did not considerably moderate this relationship.
Causal Attribution and Coping Maxims Differences between Immigrants and Non-Immigrants Suffering from Back Pain in Switzerland

PubMed Central

2016-01-01

Objectives This study aimed at investigating the relationship between causal attributions and coping maxims in people suffering from back pain. Further, it aimed at identifying in how far causal attributions and related coping maxims would defer between immigrants and non-immigrants in Switzerland. Methods Data for this study came from a larger survey study that was conducted among immigrant populations in the German- and Italian-speaking part of Switzerland. Included in the analyses were native Swiss participants, as well as Albanian- and Serbian-speaking immigrants, who had indicated to have suffered from back pain within the last 12 months prior to the study. Data was analyzed for overall 495 participants. Items for causal attributions and coping maxims were subject to factor analyses. Cultural differences were assessed with ANOVA and regression analyses. Interaction terms were included to investigate whether the relationship between causal attributions and coping maxims would differ with cultural affiliation. Results For both immigrant groups the physician’s influence on the course of their back pain was more important than for Swiss participants (p <.05). With regard to coping, both immigrant groups were more likely to agree with maxims that were related to the improvement of the back pain, as well as the acceptance of the current situation (p <.05). The only consistent interaction effect that was found indicated that being Albanian-speaking negatively moderated the relationship between physical activity as an attributed cause of back pain and all three identified coping maxims. Conclusion The study shows that differences in causal attribution and coping maxims between immigrants and non-immigrants exist. Further, the results support the assumption of an association between causal attribution and coping maxims. However cultural affiliation did not considerably moderate this relationship. PMID:27583445
Timing and Causality in the Generation of Learned Eyelid Responses

PubMed Central

Sánchez-Campusano, Raudel; Gruart, Agnès; Delgado-García, José M.

2011-01-01

The cerebellum-red nucleus-facial motoneuron (Mn) pathway has been reported as being involved in the proper timing of classically conditioned eyelid responses. This special type of associative learning serves as a model of event timing for studying the role of the cerebellum in dynamic motor control. Here, we have re-analyzed the firing activities of cerebellar posterior interpositus (IP) neurons and orbicularis oculi (OO) Mns in alert behaving cats during classical eyeblink conditioning, using a delay paradigm. The aim was to revisit the hypothesis that the IP neurons (IPns) can be considered a neuronal phase-modulating device supporting OO Mns firing with an emergent timing mechanism and an explicit correlation code during learned eyelid movements. Optimized experimental and computational tools allowed us to determine the different causal relationships (temporal order and correlation code) during and between trials. These intra- and inter-trial timing strategies expanding from sub-second range (millisecond timing) to longer-lasting ranges (interval timing) expanded the functional domain of cerebellar timing beyond motor control. Interestingly, the results supported the above-mentioned hypothesis. The causal inferences were influenced by the precise motor and pre-motor spike timing in the cause-effect interval, and, in addition, the timing of the learned responses depended on cerebellar–Mn network causality. Furthermore, the timing of CRs depended upon the probability of simulated causal conditions in the cause-effect interval and not the mere duration of the inter-stimulus interval. In this work, the close relation between timing and causality was verified. It could thus be concluded that the firing activities of IPns may be related more to the proper performance of ongoing CRs (i.e., the proper timing as a consequence of the pertinent causality) than to their generation and/or initiation. PMID:21941469

Three Cs in measurement models: causal indicators, composite indicators, and covariates.

PubMed

Bollen, Kenneth A; Bauldry, Shawn

2011-09-01

In the last 2 decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that one can classify indicators into 2 categories: effect (reflective) indicators and causal (formative) indicators. We argue that the dichotomous view is too simple. Instead, there are effect indicators and 3 types of variables on which a latent variable depends: causal indicators, composite (formative) indicators, and covariates (the "Three Cs"). Causal indicators have conceptual unity, and their effects on latent variables are structural. Covariates are not concept measures, but are variables to control to avoid bias in estimating the relations between measures and latent variables. Composite (formative) indicators form exact linear combinations of variables that need not share a concept. Their coefficients are weights rather than structural effects, and composites are a matter of convenience. The failure to distinguish the Three Cs has led to confusion and questions, such as, Are causal and formative indicators different names for the same indicator type? Should an equation with causal or formative indicators have an error term? Are the coefficients of causal indicators less stable than effect indicators? Distinguishing between causal and composite indicators and covariates goes a long way toward eliminating this confusion. We emphasize the key role that subject matter expertise plays in making these distinctions. We provide new guidelines for working with these variable types, including identification of models, scaling latent variables, parameter estimation, and validity assessment. A running empirical example on self-perceived health illustrates our major points.
A Theory of Motivation for Some Classroom Experiences.

ERIC Educational Resources Information Center

Weiner, Bernard

1979-01-01

A theory of motivation based upon attributions of causality for success and failure is offered. Three central causal dimensions are identified: stability, locus, and control; these dimensions, respectively, are linked with expectancy change, esteem-related emotions, and interpersonal judgments. A theory of motivation with implications for…
The Singaporean public beliefs about the causes of mental illness: results from a multi-ethnic population-based study.

PubMed

Pang, S; Subramaniam, M; Lee, S P; Lau, Y W; Abdin, E; Chua, B Y; Picco, L; Vaingankar, J A; Chong, S A

2017-04-03

To identify the common causal beliefs of mental illness in a multi-ethnic Southeast Asian community and describe the sociodemographic associations to said beliefs. The factor structure to the causal beliefs scale is explored. The causal beliefs relating to five different mental illnesses (alcohol abuse, depression, obsessive-compulsive disorder (OCD), dementia and schizophrenia) and desire for social distance are also investigated. Data from 3006 participants from a nationwide vignette-based study on mental health literacy were analysed using factor analysis and multiple logistic regression to address the aims. Participants answered questions related to sociodemographic information, causal beliefs of mental illness and their desire for social distance towards those with mental illness. Physical causes, psychosocial causes and personality causes were endorsed by the sample. Sociodemographic differences including ethnic, gender and age differences in causal beliefs were found in the sample. Differences in causal beliefs were shown across different mental illness vignettes though psychosocial causes was the most highly attributed cause across vignettes (endorsed by 97.9% of respondents), followed by personality causes (83.5%) and last, physical causes (37%). Physical causes were more likely to be endorsed for OCD, depression and schizophrenia. Psychosocial causes were less often endorsed for OCD. Personality causes were less endorsed for dementia but more associated with depression. The factor structure of the causal beliefs scale is not entirely the same as that found in previous research. Further research on the causal beliefs endorsed by Southeast Asian communities should be conducted to investigate other potential causes such as biogenetic factors and spiritual/supernatural causes. Mental health awareness campaigns should address causes of mental illness as a topic. Lay beliefs in the different causes must be acknowledged and it would be beneficial for the public to be informed of the causes of some of the most common mental illnesses in order to encourage help-seeking and treatment compliance.
Effective Connectivity Analysis of the Brain Network in Drivers during Actual Driving Using Near-Infrared Spectroscopy

PubMed Central

Liu, Zhian; Zhang, Ming; Xu, Gongcheng; Huo, Congcong; Tan, Qitao; Li, Zengyong; Yuan, Quan

2017-01-01

Driving a vehicle is a complex activity that requires high-level brain functions. This study aimed to assess the change in effective connectivity (EC) between the prefrontal cortex (PFC), motor-related areas (MA) and vision-related areas (VA) in the brain network among the resting, simple-driving and car-following states. Twelve young male right-handed adults were recruited to participate in an actual driving experiment. The brain delta [HbO2] signals were continuously recorded using functional near infrared spectroscopy (fNIRS) instruments. The conditional Granger causality (GC) analysis, which is a data-driven method that can explore the causal interactions among different brain areas, was performed to evaluate the EC. The results demonstrated that the hemodynamic activity level of the brain increased with an increase in the cognitive workload. The connection strength among PFC, MA and VA increased from the resting state to the simple-driving state, whereas the connection strength relatively decreased during the car-following task. The PFC in EC appeared as the causal target, while the MA and VA appeared as the causal sources. However, l-MA turned into causal targets with the subtask of car-following. These findings indicate that the hemodynamic activity level of the cerebral cortex increases linearly with increasing cognitive workload. The EC of the brain network can be strengthened by a cognitive workload, but also can be weakened by a superfluous cognitive workload such as driving with subtasks. PMID:29163083
Do people reason rationally about causally related events? Markov violations, weak inferences, and failures of explaining away.

PubMed

Rottman, Benjamin M; Hastie, Reid

2016-06-01

Making judgments by relying on beliefs about the causal relationships between events is a fundamental capacity of everyday cognition. In the last decade, Causal Bayesian Networks have been proposed as a framework for modeling causal reasoning. Two experiments were conducted to provide comprehensive data sets with which to evaluate a variety of different types of judgments in comparison to the standard Bayesian networks calculations. Participants were introduced to a fictional system of three events and observed a set of learning trials that instantiated the multivariate distribution relating the three variables. We tested inferences on chains X1→Y→X2, common cause structures X1←Y→X2, and common effect structures X1→Y←X2, on binary and numerical variables, and with high and intermediate causal strengths. We tested transitive inferences, inferences when one variable is irrelevant because it is blocked by an intervening variable (Markov Assumption), inferences from two variables to a middle variable, and inferences about the presence of one cause when the alternative cause was known to have occurred (the normative "explaining away" pattern). Compared to the normative account, in general, when the judgments should change, they change in the normative direction. However, we also discuss a few persistent violations of the standard normative model. In addition, we evaluate the relative success of 12 theoretical explanations for these deviations. Copyright © 2016 Elsevier Inc. All rights reserved.
How contrast situations affect the assignment of causality in symmetric physical settings.

PubMed

Beller, Sieghard; Bender, Andrea

2014-01-01

In determining the prime cause of a physical event, people often weight one of two entities in a symmetric physical relation as more important for bringing about the causal effect than the other. In a broad survey (Bender and Beller, 2011), we documented such weighting effects for different kinds of physical events and found that their direction and strength depended on a variety of factors. Here, we focus on one of those: adding a contrast situation that-while being formally irrelevant-foregrounds one of the factors and thus frames the task in a specific way. In two experiments, we generalize and validate our previous findings by using different stimulus material (in Experiment 1), by applying a different response format to elicit causal assignments, an analog rating scale instead of a forced-choice decision (in Experiment 2), and by eliciting explanations for the physical events in question (in both Experiments). The results generally confirm the contrast effects for both response formats; however, the effects were more pronounced with the force-choice format than with the rating format. People tended to refer to the given contrast in their explanations, which validates our manipulation. Finally, people's causal assignments are reflected in the type of explanation given in that contrast and property explanations were associated with biased causal assignments, whereas relational explanations were associated with unbiased assignments. In the discussion, we pick up the normative questions of whether or not these contrast effects constitute a bias in causal reasoning.
CauseMap: fast inference of causality from complex time series.

PubMed

Maher, M Cyrus; Hernandez, Ryan D

2015-01-01

Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data. Results. Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM), a method for establishing causality from long time series data (≳25 observations). Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens' Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement CCM in Julia, a high-performance programming language designed for facile technical computing. Our software package, CauseMap, is platform-independent and freely available as an official Julia package. Conclusions. CauseMap is an efficient implementation of a state-of-the-art algorithm for detecting causality from time series data. We believe this tool will be a valuable resource for biomedical research and personalized medicine.
Causality as a Rigorous Notion and Quantitative Causality Analysis with Time Series

NASA Astrophysics Data System (ADS)

Liang, X. S.

2017-12-01

Given two time series, can one faithfully tell, in a rigorous and quantitative way, the cause and effect between them? Here we show that this important and challenging question (one of the major challenges in the science of big data), which is of interest in a wide variety of disciplines, has a positive answer. Particularly, for linear systems, the maximal likelihood estimator of the causality from a series X2 to another series X1, written T2→1, turns out to be concise in form: T2→1 = [C11 C12 C2,d1 — C112 C1,d1] / [C112 C22 — C11C122] where Cij (i,j=1,2) is the sample covariance between Xi and Xj, and Ci,dj the covariance between Xi and ΔXj/Δt, the difference approximation of dXj/dt using the Euler forward scheme. An immediate corollary is that causation implies correlation, but not vice versa, resolving the long-standing debate over causation versus correlation. The above formula has been validated with touchstone series purportedly generated with one-way causality that evades the classical approaches such as Granger causality test and transfer entropy analysis. It has also been applied successfully to the investigation of many real problems. Through a simple analysis with the stock series of IBM and GE, an unusually strong one-way causality is identified from the former to the latter in their early era, revealing to us an old story, which has almost faded into oblivion, about "Seven Dwarfs" competing with a "Giant" for the computer market. Another example presented here regards the cause-effect relation between the two climate modes, El Niño and Indian Ocean Dipole (IOD). In general, these modes are mutually causal, but the causality is asymmetric. To El Niño, the information flowing from IOD manifests itself as a propagation of uncertainty from the Indian Ocean. In the third example, an unambiguous one-way causality is found between CO2 and the global mean temperature anomaly. While it is confirmed that CO2 indeed drives the recent global warming, on paleoclimate scales the cause-effect relation may be completely reversed. Key words: Causation, Information flow, Uncertainty Generation, El Niño, IOD, CO2/Global warming Reference : Liang, 2014: Unraveling the cause-effect relation between time series. PRE 90, 052150 News Report: http://scitation.aip.org/content/aip/magazine/physicstoday/news/10.1063/PT.5.7124
Causal localizations in relativistic quantum mechanics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Castrigiano, Domenico P. L., E-mail: castrig@ma.tum.de; Leiseifer, Andreas D., E-mail: andreas.leiseifer@tum.de

2015-07-15

Causal localizations describe the position of quantum systems moving not faster than light. They are constructed for the systems with finite spinor dimension. At the center of interest are the massive relativistic systems. For every positive mass, there is the sequence of Dirac tensor-localizations, which provides a complete set of inequivalent irreducible causal localizations. They obey the principle of special relativity and are fully Poincaré covariant. The boosters are determined by the causal position operator and the other Poincaré generators. The localization with minimal spinor dimension is the Dirac localization. Thus, the Dirac equation is derived here as a meremore » consequence of the principle of causality. Moreover, the higher tensor-localizations, not known so far, follow from Dirac’s localization by a simple construction. The probability of localization for positive energy states results to be described by causal positive operator valued (PO-) localizations, which are the traces of the causal localizations on the subspaces of positive energy. These causal Poincaré covariant PO-localizations for every irreducible massive relativistic system were, all the more, not known before. They are shown to be separated. Hence, the positive energy systems can be localized within every open region by a suitable preparation as accurately as desired. Finally, the attempt is made to provide an interpretation of the PO-localization operators within the frame of conventional quantum mechanics attributing an important role to the negative energy states.« less
Causal reports: Context-dependent contributions of intuitive physics and visual impressions of launching.

PubMed

Vicovaro, Michele

2018-05-01

Everyday causal reports appear to be based on a blend of perceptual and cognitive processes. Causality can sometimes be perceived automatically through low-level visual processing of stimuli, but it can also be inferred on the basis of an intuitive understanding of the physical mechanism that underlies an observable event. We investigated how visual impressions of launching and the intuitive physics of collisions contribute to the formation of explicit causal responses. In Experiment 1, participants observed collisions between realistic objects differing in apparent material and hence implied mass, whereas in Experiment 2, participants observed collisions between abstract, non-material objects. The results of Experiment 1 showed that ratings of causality were mainly driven by the intuitive physics of collisions, whereas the results of Experiment 2 provide some support to the hypothesis that ratings of causality were mainly driven by visual impressions of launching. These results suggest that stimulus factors and experimental design factors - such as the realism of the stimuli and the variation in the implied mass of the colliding objects - may determine the relative contributions of perceptual and post-perceptual cognitive processes to explicit causal responses. A revised version of the impetus transmission heuristic provides a satisfactory explanation for these results, whereas the hypothesis that causal responses and intuitive physics are based on the internalization of physical laws does not. Copyright © 2018 Elsevier B.V. All rights reserved.
Functional Brain Networks and White Matter Underlying Theory-of-Mind in Autism

PubMed Central

Kana, Rajesh K.; Libero, Lauren E.; Hu, Christi P.; Deshpande, Hrishikesh D.; Colburn, Jeffrey S.

2014-01-01

Human beings constantly engage in attributing causal explanations to one’s own and to others’ actions, and theory-of-mind (ToM) is critical in making such inferences. Although children learn causal attribution early in development, children with autism spectrum disorders (ASDs) are known to have impairments in the development of intentional causality. This functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) study investigated the neural correlates of physical and intentional causal attribution in people with ASDs. In the fMRI scanner, 15 adolescents and adults with ASDs and 15 age- and IQ-matched typically developing peers made causal judgments about comic strips presented randomly in an event-related design. All participants showed robust activation in bilateral posterior superior temporal sulcus at the temporo-parietal junction (TPJ) in response to intentional causality. Participants with ASDs showed lower activation in TPJ, right inferior frontal gyrus and left premotor cortex. Significantly weaker functional connectivity was also found in the ASD group between TPJ and motor areas during intentional causality. DTI data revealed significantly reduced fractional anisotropy in ASD participants in white matter underlying the temporal lobe. In addition to underscoring the role of TPJ in ToM, this study found an interaction between motor simulation and mentalizing systems in intentional causal attribution and its possible discord in autism. PMID:22977198
Causal localizations in relativistic quantum mechanics

NASA Astrophysics Data System (ADS)

Castrigiano, Domenico P. L.; Leiseifer, Andreas D.

2015-07-01

Causal localizations describe the position of quantum systems moving not faster than light. They are constructed for the systems with finite spinor dimension. At the center of interest are the massive relativistic systems. For every positive mass, there is the sequence of Dirac tensor-localizations, which provides a complete set of inequivalent irreducible causal localizations. They obey the principle of special relativity and are fully Poincaré covariant. The boosters are determined by the causal position operator and the other Poincaré generators. The localization with minimal spinor dimension is the Dirac localization. Thus, the Dirac equation is derived here as a mere consequence of the principle of causality. Moreover, the higher tensor-localizations, not known so far, follow from Dirac's localization by a simple construction. The probability of localization for positive energy states results to be described by causal positive operator valued (PO-) localizations, which are the traces of the causal localizations on the subspaces of positive energy. These causal Poincaré covariant PO-localizations for every irreducible massive relativistic system were, all the more, not known before. They are shown to be separated. Hence, the positive energy systems can be localized within every open region by a suitable preparation as accurately as desired. Finally, the attempt is made to provide an interpretation of the PO-localization operators within the frame of conventional quantum mechanics attributing an important role to the negative energy states.
Siblings within Families: Levels of Analysis and Patterns of Influence

ERIC Educational Resources Information Center

Jenkins, Jennifer; Dunn, Judy

2009-01-01

The study of siblings has become increasingly central to developmental science. Sibling relationships have unique effects on development, and sibling designs allow researchers to isolate causal mechanisms in development. This volume emphasizes causal mechanisms in the social domain. We review the preceding chapters in relation to six topics: a…
Foreign Language Listening Anxiety and Listening Performance: Conceptualizations and Causal Relationships

ERIC Educational Resources Information Center

Zhang, Xian

2013-01-01

This study used structural equation modeling to explore the possible causal relations between foreign language (English) listening anxiety and English listening performance. Three hundred participants learning English as a foreign language (FL) completed the foreign language listening anxiety scale (FLLAS) and IELTS test twice with an interval of…
Resources, Instruction, and Research

ERIC Educational Resources Information Center

Cohen, David K.; Raudenbush, Stephen W.; Ball, Deborah Loewenberg

2003-01-01

Many researchers who study the relations between school resources and student achievement have worked from a causal model, which typically is implicit. In this model, some resource or set of resources is the causal variable and student achievement is the outcome. In a few recent, more nuanced versions, resource effects depend on intervening…
Extending Attribution Theory: Considering Students' Perceived Control of the Attribution Process

ERIC Educational Resources Information Center

Fishman, Evan J.; Husman, Jenefer

2017-01-01

Research in attribution theory has shown that students' causal thinking profoundly affects their learning and motivational outcomes. Very few studies, however, have explored how students' attribution-related beliefs influence the causal thought process. The present study used the perceived control of the attribution process (PCAP) model to examine…
Children's and Adolescents' Thoughts on Pollution: Cognitive Abilities Required to Understand Environmental Systems

ERIC Educational Resources Information Center

Rodríguez, Manuel; Kohen, Raquel; Delval, Juan

2015-01-01

Pollution phenomena are complex systems in which different parts are integrated by means of causal and temporal relationships. To understand pollution, children must develop some cognitive abilities related to system thinking and temporal and causal inferential reasoning. These cognitive abilities constrain and guide how children understand…
Music and Spatial Task Performance: A Causal Relationship.

ERIC Educational Resources Information Center

Rauscher, Frances H.; And Others

This research paper reports on testing the hypothesis that music and spatial task performance are causally related. Two complementary studies are presented that replicate and explore previous findings. One study of college students showed that listening to a Mozart sonata induces subsequent short-term spatial reasoning facilitation and tested the…
Time, frequency, and time-varying Granger-causality measures in neuroscience.

PubMed

Cekic, Sezen; Grandjean, Didier; Renaud, Olivier

2018-05-20

This article proposes a systematic methodological review and an objective criticism of existing methods enabling the derivation of time, frequency, and time-varying Granger-causality statistics in neuroscience. The capacity to describe the causal links between signals recorded at different brain locations during a neuroscience experiment is indeed of primary interest for neuroscientists, who often have very precise prior hypotheses about the relationships between recorded brain signals. The increasing interest and the huge number of publications related to this topic calls for this systematic review, which describes the very complex methodological aspects underlying the derivation of these statistics. In this article, we first present a general framework that allows us to review and compare Granger-causality statistics in the time domain, and the link with transfer entropy. Then, the spectral and the time-varying extensions are exposed and discussed together with their estimation and distributional properties. Although not the focus of this article, partial and conditional Granger causality, dynamical causal modelling, directed transfer function, directed coherence, partial directed coherence, and their variant are also mentioned. Copyright © 2018 John Wiley & Sons, Ltd.
Loop Analysis of Causal Feedback in Epidemiology: An Illustration Relating To Urban Neighborhoods and Resident Depressive Experiences

PubMed Central

2008-01-01

The causal feedback implied by urban neighborhood conditions that shape human health experiences, that in turn shape neighborhood conditions through a complex causal web, raises a challenge for traditional epidemiological causal analyses. This article introduces the loop analysis method, and builds off of a core loop model linking neighborhood property vacancy rate, resident depressive symptoms, rate of neighborhood death, and rate of neighborhood exit in a feedback network. I justify and apply loop analysis to the specific example of depressive symptoms and abandoned urban residential property to show how inquiries into the behavior of causal systems can answer different kinds of hypotheses, and thereby compliment those of causal modeling using statistical models. Neighborhood physical conditions that are only indirectly influenced by depressive symptoms may nevertheless manifest in the mental health experiences of their residents; conversely, neighborhood physical conditions may be a significant mental health risk for the population of neighborhood residents. I find that participatory greenspace programs are likely to produce adaptive responses in depressive symptoms and different neighborhood conditions, which are different in character to non-participatory greenspace interventions. PMID:17706851

Links between causal effects and causal association for surrogacy evaluation in a gaussian setting.

PubMed

Conlon, Anna; Taylor, Jeremy; Li, Yun; Diaz-Ordaz, Karla; Elliott, Michael

2017-11-30

Two paradigms for the evaluation of surrogate markers in randomized clinical trials have been proposed: the causal effects paradigm and the causal association paradigm. Each of these paradigms rely on assumptions that must be made to proceed with estimation and to validate a candidate surrogate marker (S) for the true outcome of interest (T). We consider the setting in which S and T are Gaussian and are generated from structural models that include an unobserved confounder. Under the assumed structural models, we relate the quantities used to evaluate surrogacy within both the causal effects and causal association frameworks. We review some of the common assumptions made to aid in estimating these quantities and show that assumptions made within one framework can imply strong assumptions within the alternative framework. We demonstrate that there is a similarity, but not exact correspondence between the quantities used to evaluate surrogacy within each framework, and show that the conditions for identifiability of the surrogacy parameters are different from the conditions, which lead to a correspondence of these quantities. Copyright © 2017 John Wiley & Sons, Ltd.
Generalizing entanglement

NASA Astrophysics Data System (ADS)

Jia, Ding

2017-12-01

The expected indefinite causal structure in quantum gravity poses a challenge to the notion of entanglement: If two parties are in an indefinite causal relation of being causally connected and not, can they still be entangled? If so, how does one measure the amount of entanglement? We propose to generalize the notions of entanglement and entanglement measure to address these questions. Importantly, the generalization opens the path to study quantum entanglement of states, channels, networks, and processes with definite or indefinite causal structure in a unified fashion, e.g., we show that the entanglement distillation capacity of a state, the quantum communication capacity of a channel, and the entanglement generation capacity of a network or a process are different manifestations of one and the same entanglement measure.
Generalized group field theories and quantum gravity transition amplitudes

NASA Astrophysics Data System (ADS)

Oriti, Daniele

2006-03-01

We construct a generalized formalism for group field theories, in which the domain of the field is extended to include additional proper time variables, as well as their conjugate mass variables. This formalism allows for different types of quantum gravity transition amplitudes in perturbative expansion, and we show how both causal spin foam models and the usual a-causal ones can be derived from it, within a sum over triangulations of all topologies. We also highlight the relation of the so-derived causal transition amplitudes with simplicial gravity actions.
Three Cs in Measurement Models: Causal Indicators, Composite Indicators, and Covariates

PubMed Central

Bollen, Kenneth A.; Bauldry, Shawn

2013-01-01

In the last two decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that we can classify indicators into two categories, effect (reflective) indicators and causal (formative) indicators. This paper argues that the dichotomous view is too simple. Instead, there are effect indicators and three types of variables on which a latent variable depends: causal indicators, composite (formative) indicators, and covariates (the “three Cs”). Causal indicators have conceptual unity and their effects on latent variables are structural. Covariates are not concept measures, but are variables to control to avoid bias in estimating the relations between measures and latent variable(s). Composite (formative) indicators form exact linear combinations of variables that need not share a concept. Their coefficients are weights rather than structural effects and composites are a matter of convenience. The failure to distinguish the “three Cs” has led to confusion and questions such as: are causal and formative indicators different names for the same indicator type? Should an equation with causal or formative indicators have an error term? Are the coefficients of causal indicators less stable than effect indicators? Distinguishing between causal and composite indicators and covariates goes a long way toward eliminating this confusion. We emphasize the key role that subject matter expertise plays in making these distinctions. We provide new guidelines for working with these variable types, including identification of models, scaling latent variables, parameter estimation, and validity assessment. A running empirical example on self-perceived health illustrates our major points. PMID:21767021
Just Do It? Investigating the Gap between Prediction and Action in Toddlers Causal Inferences

ERIC Educational Resources Information Center

Bonawitz, Elizabeth Baraff; Ferranti, Darlene; Saxe, Rebecca; Gopnik, Alison; Meltzoff, Andrew N.; Woodward, James; Schulz, Laura E.

2010-01-01

Adults' causal representations integrate information about predictive relations and the possibility of effective intervention; if one event reliably predicts another, adults can represent the possibility that acting to bring about the first event might generate the second. Here we show that although toddlers (mean age: 24 months) readily learn…
Attributions for Pride, Anger, and Guilt among Incarcerated Minority Adolescents.

ERIC Educational Resources Information Center

Hudley-Paul, Cynthia A.

Two studies investigate causal attributions among minority adolescents. The first investigates attributions for the emotions of anger, pride, and guilt among 26 incarcerated male adolescents. Relatively few causes are found for anger and guilt, and a larger variety of causes are cited for pride. A follow-up study then compares causal attributions…
Cognitive Development Masks Support for Attributional Style Models of Depression in Children and Adolescents

ERIC Educational Resources Information Center

Weitlauf, Amy S.; Cole, David A.

2012-01-01

Attributional style models of depression in adults (Abramson et al. 1989, 1978) have been adapted for use with children; however, most applications do not consider that children's understanding of causal relations may be qualitatively different from that of adults. If children's causal attributions depend on children's level of cognitive…
Learning a Theory of Causality

ERIC Educational Resources Information Center

Goodman, Noah D.; Ullman, Tomer D.; Tenenbaum, Joshua B.

2011-01-01

The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be…
Causal Inference and Language Comprehension: Event-Related Potential Investigations

ERIC Educational Resources Information Center

Davenport, Tristan S.

2014-01-01

The most important information conveyed by language is often contained not in the utterance itself, but in the interaction between the utterance and the comprehender's knowledge of the world and the current situation. This dissertation uses psycholinguistic methods to explore the effects of a common type of inference--causal inference--on language…
A Causal Modelling Approach to the Development of Theory-Based Behaviour Change Programmes for Trial Evaluation

ERIC Educational Resources Information Center

Hardeman, Wendy; Sutton, Stephen; Griffin, Simon; Johnston, Marie; White, Anthony; Wareham, Nicholas J.; Kinmonth, Ann Louise

2005-01-01

Theory-based intervention programmes to support health-related behaviour change aim to increase health impact and improve understanding of mechanisms of behaviour change. However, the science of intervention development remains at an early stage. We present a causal modelling approach to developing complex interventions for evaluation in…
Should herbs take all the blame? Causality assessment of a serious thrombocytopenia event.

PubMed

Lai, Jung-Nien; Hsieh, Shu-Ching; Chen, Pau-Chung; Chen, Huey-Jen; Wang, Jung-Der

2010-11-01

With the increasing use of herbal medicines, the causality assessment of adverse drug-related reactions becomes more complicated because of the concomitant use of herbs and conventional medications. Epidemiological causal inference can be a central feature of such judgment but may be insufficient. Other scientific considerations include study design, bias, confounding, and measurement issues. The approach of this study is to establish an active safety surveillance system for finished herbal products (FHPs) and to review each adverse event regularly. A single case of serious thrombocytopenia was found in 136 subjects taking FHPs on a clinical trial for 12 weeks, for which the cause was sought. Because at the end of the first month the patient's platelet counts were normal and the thrombocytopenia developed after the co-medication with conventional drugs, it was suspected that the thrombocytopenia might not be attributed to the use of FHP. This report summarizes the criteria of causality assessment under mixed use of herbs and conventional medicine and recommends a feasible process for careful evaluation of adverse drug reactions related to all herbal medicine.
Beyond Markov: Accounting for independence violations in causal reasoning.

PubMed

Rehder, Bob

2018-06-01

Although many theories of causal cognition are based on causal graphical models, a key property of such models-the independence relations stipulated by the Markov condition-is routinely violated by human reasoners. This article presents three new accounts of those independence violations, accounts that share the assumption that people's understanding of the correlational structure of data generated from a causal graph differs from that stipulated by causal graphical model framework. To distinguish these models, experiments assessed how people reason with causal graphs that are larger than those tested in previous studies. A traditional common cause network (Y 1 ←X→Y 2 ) was extended so that the effects themselves had effects (Z 1 ←Y 1 ←X→Y 2 →Z 2 ). A traditional common effect network (Y 1 →X←Y 2 ) was extended so that the causes themselves had causes (Z 1 →Y 1 →X←Y 2 ←Z 2 ). Subjects' inferences were most consistent with the beta-Q model in which consistent states of the world-those in which variables are either mostly all present or mostly all absent-are viewed as more probable than stipulated by the causal graphical model framework. Substantial variability in subjects' inferences was also observed, with the result that substantial minorities of subjects were best fit by one of the other models (the dual prototype or a leaky gate models). The discrepancy between normative and human causal cognition stipulated by these models is foundational in the sense that they locate the error not in people's causal reasoning but rather in their causal representations. As a result, they are applicable to any cognitive theory grounded in causal graphical models, including theories of analogy, learning, explanation, categorization, decision-making, and counterfactual reasoning. Preliminary evidence that independence violations indeed generalize to other judgment types is presented. Copyright © 2018 Elsevier Inc. All rights reserved.
Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data

PubMed Central

Havlicek, Martin; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D.

2015-01-01

Increasing interest in understanding dynamic interactions of brain neural networks leads to formulation of sophisticated connectivity analysis methods. Recent studies have applied Granger causality based on standard multivariate autoregressive (MAR) modeling to assess the brain connectivity. Nevertheless, one important flaw of this commonly proposed method is that it requires the analyzed time series to be stationary, whereas such assumption is mostly violated due to the weakly nonstationary nature of functional magnetic resonance imaging (fMRI) time series. Therefore, we propose an approach to dynamic Granger causality in the frequency domain for evaluating functional network connectivity in fMRI data. The effectiveness and robustness of the dynamic approach was significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling. In our method, the functional networks were first detected by independent component analysis (ICA), a computational method for separating a multivariate signal into maximally independent components. Then the measure of Granger causality was evaluated using generalized partial directed coherence that is suitable for bivariate as well as multivariate data. Moreover, this metric provides identification of causal relation in frequency domain, which allows one to distinguish the frequency components related to the experimental paradigm. The procedure of evaluating Granger causality via dynamic MAR was demonstrated on simulated time series as well as on two sets of group fMRI data collected during an auditory sensorimotor (SM) or auditory oddball discrimination (AOD) tasks. Finally, a comparison with the results obtained from a standard time-invariant MAR model was provided. PMID:20561919
Impairments in action-outcome learning in schizophrenia.

PubMed

Morris, Richard W; Cyrzon, Chad; Green, Melissa J; Le Pelley, Mike E; Balleine, Bernard W

2018-03-03

Learning the causal relation between actions and their outcomes (AO learning) is critical for goal-directed behavior when actions are guided by desire for the outcome. This can be contrasted with habits that are acquired by reinforcement and primed by prevailing stimuli, in which causal learning plays no part. Recently, we demonstrated that goal-directed actions are impaired in schizophrenia; however, whether this deficit exists alongside impairments in habit or reinforcement learning is unknown. The present study distinguished deficits in causal learning from reinforcement learning in schizophrenia. We tested people with schizophrenia (SZ, n = 25) and healthy adults (HA, n = 25) in a vending machine task. Participants learned two action-outcome contingencies (e.g., push left to get a chocolate M&M, push right to get a cracker), and they also learned one contingency was degraded by delivery of noncontingent outcomes (e.g., free M&Ms), as well as changes in value by outcome devaluation. Both groups learned the best action to obtain rewards; however, SZ did not distinguish the more causal action when one AO contingency was degraded. Moreover, action selection in SZ was insensitive to changes in outcome value unless feedback was provided, and this was related to the deficit in AO learning. The failure to encode the causal relation between action and outcome in schizophrenia occurred without any apparent deficit in reinforcement learning. This implies that poor goal-directed behavior in schizophrenia cannot be explained by a more primary deficit in reward learning such as insensitivity to reward value or reward prediction errors.
Causal structures in Gauss-Bonnet gravity

NASA Astrophysics Data System (ADS)

Izumi, Keisuke

2014-08-01

We analyze causal structures in Gauss-Bonnet gravity. It is known that Gauss-Bonnet gravity potentially has superluminal propagation of gravitons due to its noncanonical kinetic terms. In a theory with superluminal modes, an analysis of causality based on null curves makes no sense, and thus, we need to analyze them in a different way. In this paper, using the method of the characteristics, we analyze the causal structure in Gauss-Bonnet gravity. We have the result that, on a Killing horizon, gravitons can propagate in the null direction tangent to the Killing horizon. Therefore, a Killing horizon can be a causal edge as in the case of general relativity; i.e. a Killing horizon is the "event horizon" in the sense of causality. We also analyze causal structures on nonstationary solutions with (D-2)-dimensional maximal symmetry, including spherically symmetric and flat spaces. If the geometrical null energy condition, RABNANB≥0 for any null vector NA, is satisfied, the radial velocity of gravitons must be less than or equal to that of light. However, if the geometrical null energy condition is violated, gravitons can propagate faster than light. Hence, on an evaporating black hole where the geometrical null energy condition is expected not to hold, classical gravitons can escape from the "black hole" defined with null curves. That is, the causal structures become nontrivial. It may be one of the possible solutions for the information loss paradox of evaporating black holes.
Implementation and reporting of causal mediation analysis in 2015: a systematic review in epidemiological studies.

PubMed

Liu, Shao-Hsien; Ulbricht, Christine M; Chrysanthopoulou, Stavroula A; Lapane, Kate L

2016-07-20

Causal mediation analysis is often used to understand the impact of variables along the causal pathway of an occurrence relation. How well studies apply and report the elements of causal mediation analysis remains unknown. We systematically reviewed epidemiological studies published in 2015 that employed causal mediation analysis to estimate direct and indirect effects of observed associations between an exposure on an outcome. We identified potential epidemiological studies through conducting a citation search within Web of Science and a keyword search within PubMed. Two reviewers independently screened studies for eligibility. For eligible studies, one reviewer performed data extraction, and a senior epidemiologist confirmed the extracted information. Empirical application and methodological details of the technique were extracted and summarized. Thirteen studies were eligible for data extraction. While the majority of studies reported and identified the effects of measures, most studies lacked sufficient details on the extent to which identifiability assumptions were satisfied. Although most studies addressed issues of unmeasured confounders either from empirical approaches or sensitivity analyses, the majority did not examine the potential bias arising from the measurement error of the mediator. Some studies allowed for exposure-mediator interaction and only a few presented results from models both with and without interactions. Power calculations were scarce. Reporting of causal mediation analysis is varied and suboptimal. Given that the application of causal mediation analysis will likely continue to increase, developing standards of reporting of causal mediation analysis in epidemiological research would be prudent.
Architecture of Explanatory Inference in the Human Prefrontal Cortex

PubMed Central

Barbey, Aron K.; Patterson, Richard

2011-01-01

Causal reasoning is a ubiquitous feature of human cognition. We continuously seek to understand, at least implicitly and often explicitly, the causal scenarios in which we live, so that we may anticipate what will come next, plan a potential response and envision its outcome, decide among possible courses of action in light of their probable outcomes, make midstream adjustments in our goal-related activities as our situation changes, and so on. A considerable body of research shows that the lateral prefrontal cortex (PFC) is crucial for causal reasoning, but also that there are significant differences in the manner in which ventrolateral PFC, dorsolateral PFC, and anterolateral PFC support causal reasoning. We propose, on the basis of research on the evolution, architecture, and functional organization of the lateral PFC, a general framework for understanding its roles in the many and varied sorts of causal reasoning carried out by human beings. Specifically, the ventrolateral PFC supports the generation of basic causal explanations and inferences; dorsolateral PFC supports the evaluation of these scenarios in light of some given normative standard (e.g., of plausibility or correctness in light of real or imagined causal interventions); and anterolateral PFC supports explanation and inference at an even higher level of complexity, coordinating the processes of generation and evaluation with further cognitive processes, and especially with computations of hedonic value and emotional implications of possible behavioral scenarios – considerations that are often critical both for understanding situations causally and for deciding about our own courses of action. PMID:21845182
Automated interviews on clinical case reports to elicit directed acyclic graphs.

PubMed

Luciani, Davide; Stefanini, Federico M

2012-05-01

Setting up clinical reports within hospital information systems makes it possible to record a variety of clinical presentations. Directed acyclic graphs (Dags) offer a useful way of representing causal relations in clinical problem domains and are at the core of many probabilistic models described in the medical literature, like Bayesian networks. However, medical practitioners are not usually trained to elicit Dag features. Part of the difficulty lies in the application of the concept of direct causality before selecting all the causal variables of interest for a specific patient. We designed an automated interview to tutor medical doctors in the development of Dags to represent their understanding of clinical reports. Medical notions were analyzed to find patterns in medical reasoning that can be followed by algorithms supporting the elicitation of causal Dags. Clinical relevance was defined to help formulate only relevant questions by driving an expert's attention towards variables causally related to nodes already inserted in the graph. Key procedural features of the proposed interview are described by four algorithms. The automated interview comprises questions on medical notions, phrased in medical terms. The first elicitation session produces questions concerning the patient's chief complaints and the outcomes related to diseases serving as diagnostic hypotheses, their observable manifestations and risk factors. The second session focuses on questions that refine the initial causal paths by considering syndromes, dysfunctions, pathogenic anomalies, biases and effect modifiers. A case study concerning a gastro-enterological problem and one dealing with an infected patient illustrate the output produced by the algorithms, depending on the answers provided by the doctor. The proposed elicitation framework is characterized by strong consistency with medical background and by a progressive introduction of relevant medical topics. Revision and testing of the subjectively elicited Dag is performed by matching the collected answers with the evidence included in accepted sources of biomedical knowledge. Copyright © 2011 Elsevier B.V. All rights reserved.
Understanding bias in relationships between the food environment and diet quality: the Coronary Artery Risk Development in Young Adults (CARDIA) study.

PubMed

Rummo, Pasquale E; Guilkey, David K; Ng, Shu Wen; Meyer, Katie A; Popkin, Barry M; Reis, Jared P; Shikany, James M; Gordon-Larsen, Penny

2017-12-01

The relationship between food environment exposures and diet behaviours is unclear, possibly because the majority of studies ignore potential residual confounding. We used 20 years (1985-1986, 1992-1993 2005-2006) of data from the Coronary Artery Risk Development in Young Adults (CARDIA) study across four US cities (Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; Oakland, California) and instrumental variables (IV) regression to obtain causal estimates of longitudinal associations between the percentage of neighbourhood food outlets (per total food outlets within 1 km network distance of respondent residence) and an a priori diet quality score, with higher scores indicating higher diet quality. To assess the presence and magnitude of bias related to residual confounding, we compared results from causal models (IV regression) to non-causal models, including ordinary least squares regression, which does not account for residual confounding at all and fixed-effects regression, which only controls for time-invariant unmeasured characteristics. The mean diet quality score across follow-up was 63.4 (SD=12.7). A 10% increase in fast food restaurants (relative to full-service restaurants) was associated with a lower diet quality score over time using IV regression (β=-1.01, 95% CI -1.99 to -0.04); estimates were attenuated using non-causal models. The percentage of neighbourhood convenience and grocery stores (relative to supermarkets) was not associated with diet quality in any model, but estimates from non-causal models were similarly attenuated compared with causal models. Ignoring residual confounding may generate biased estimated effects of neighbourhood food outlets on diet outcomes and may have contributed to weak findings in the food environment literature. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
Recognising discourse causality triggers in the biomedical domain.

PubMed

Mihăilă, Claudiu; Ananiadou, Sophia

2013-12-01

Current domain-specific information extraction systems represent an important resource for biomedical researchers, who need to process vast amounts of knowledge in a short time. Automatic discourse causality recognition can further reduce their workload by suggesting possible causal connections and aiding in the curation of pathway models. We describe here an approach to the automatic identification of discourse causality triggers in the biomedical domain using machine learning. We create several baselines and experiment with and compare various parameter settings for three algorithms, i.e. Conditional Random Fields (CRF), Support Vector Machines (SVM) and Random Forests (RF). We also evaluate the impact of lexical, syntactic, and semantic features on each of the algorithms, showing that semantics improves the performance in all cases. We test our comprehensive feature set on two corpora containing gold standard annotations of causal relations, and demonstrate the need for more gold standard data. The best performance of 79.35% F-score is achieved by CRFs when using all three feature types.

"Head take you": causal attributions of mental illness in Jamaica.

PubMed

Arthur, Carlotta M; Whitley, Rob

2015-02-01

Causal attributions are a key factor in explanatory models of illness; however, little research on causal attributions of mental illness has been conducted in developing nations in the Caribbean, including Jamaica. Explanatory models of mental illness may be important in understanding illness experience and be a crucial factor in mental health service seeking and utilization. We explored causal attributions of mental illness in Jamaica by conducting 20 focus groups, including 16 community samples, 2 patient samples, and 2 samples of caregivers of patients, with a total of 159 participants. The 5 most commonly endorsed causal attributions of mental illness are discussed: (a) drug-related causes, including ganja (marijuana); (b) biological causes, such as chemical imbalance, familial transmission, and "blood"; (c) psychological causes, including stress and thinking too much; (d) social causes, such as relationship problems and job loss; and (e) spiritual or religious causes, including Obeah. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.
Causal essentialism in kinds.

PubMed

Ahn, Woo-kyoung; Taylor, Eric G; Kato, Daniel; Marsh, Jessecae K; Bloom, Paul

2013-06-01

The current study examines causal essentialism, derived from psychological essentialism of concepts. We examine whether people believe that members of a category share some underlying essence that is both necessary and sufficient for category membership and that also causes surface features. The main claim is that causal essentialism is restricted to categories that correspond to our intuitive notions of existing kinds and hence is more attenuated for categories that are based on arbitrary criteria. Experiments 1 and 3 found that people overtly endorse causal essences in nonarbitrary kinds but are less likely to do so for arbitrary categories. Experiments 2 and 4 found that people were more willing to generalize a member's known causal relations (or lack thereof) when dealing with a kind than when dealing with an arbitrary category. These differences between kinds and arbitrary categories were found across various domains-not only for categories of living things, but also for artefacts. These findings have certain real-world implications, including how people make sense of mental disorders that are treated as real kinds.
[FROM STATISTICAL ASSOCIATIONS TO SCIENTIFIC CAUSALITY].

PubMed

Golan, Daniel; Linn, Shay

2015-06-01

The pathogenesis of most chronic diseases is complex and probably involves the interaction of multiple genetic and environmental risk factors. One way to learn about disease triggers is from statistically significant associations in epidemiological studies. However, associations do not necessarily prove causation. Associations can commonly result from bias, confounding and reverse causation. Several paradigms for causality inference have been developed. Henle-Koch postulates are mainly applied for infectious diseases. Austin Bradford Hill's criteria may serve as a practical tool to weigh the evidence regarding the probability that a single new risk factor for a given disease is indeed causal. These criteria are irrelevant for estimating the causal relationship between exposure to a risk factor and disease whenever biological causality has been previously established. Thus, it is highly probable that past exposure of an individual to definite carcinogens is related to his cancer, even without proving an association between this exposure and cancer in his group. For multifactorial diseases, Rothman's model of interacting sets of component causes can be applied.
Time-varying causal network of the Korean financial system based on firm-specific risk premiums

NASA Astrophysics Data System (ADS)

Song, Jae Wook; Ko, Bonggyun; Cho, Poongjin; Chang, Woojin

2016-09-01

The aim of this paper is to investigate the Korean financial system based on time-varying causal network. We discover many stylized facts by utilizing the firm-specific risk premiums for measuring the causality direction from a firm to firm. At first, we discover that the interconnectedness of causal network is affected by the outbreak of financial events; the co-movement of firm-specific risk premium is strengthened after each positive event, and vice versa. Secondly, we find that the major sector of the Korean financial system is the Depositories, and the financial reform in June-2011 achieves its purpose by weakening the power of risk-spillovers of Broker-Dealers. Thirdly, we identify that the causal network is a small-world network with scale-free topology where the power-law exponents of out-Degree and negative event are more significant than those of in-Degree and positive event. Lastly, we discuss that the current aspects of causal network are closely related to the long-term future scenario of the KOSPI Composite index where the direction and stability are significantly affected by the power of risk-spillovers and the power-law exponents of degree distributions, respectively.
ICSNPathway: identify candidate causal SNPs and pathways from genome-wide association study by one analytical framework.

PubMed

Zhang, Kunlin; Chang, Suhua; Cui, Sijia; Guo, Liyuan; Zhang, Liuyan; Wang, Jing

2011-07-01

Genome-wide association study (GWAS) is widely utilized to identify genes involved in human complex disease or some other trait. One key challenge for GWAS data interpretation is to identify causal SNPs and provide profound evidence on how they affect the trait. Currently, researches are focusing on identification of candidate causal variants from the most significant SNPs of GWAS, while there is lack of support on biological mechanisms as represented by pathways. Although pathway-based analysis (PBA) has been designed to identify disease-related pathways by analyzing the full list of SNPs from GWAS, it does not emphasize on interpreting causal SNPs. To our knowledge, so far there is no web server available to solve the challenge for GWAS data interpretation within one analytical framework. ICSNPathway is developed to identify candidate causal SNPs and their corresponding candidate causal pathways from GWAS by integrating linkage disequilibrium (LD) analysis, functional SNP annotation and PBA. ICSNPathway provides a feasible solution to bridge the gap between GWAS and disease mechanism study by generating hypothesis of SNP → gene → pathway(s). The ICSNPathway server is freely available at http://icsnpathway.psych.ac.cn/.
Aging and Retrospective Revaluation of Causal Learning

PubMed Central

Mutter, Sharon A.; Atchley, Anthony R.; Plumlee, Leslie M.

2011-01-01

In a two-stage causal learning task, young and older participants first learned which foods presented in compound were followed by an allergic reaction (e.g., STEAK - BEANS → REACTION) and then the causal efficacy of one food from these compounds was revalued (e.g., BEANS → NO REACTION). In Experiment 1, unrelated food pairs were used and although there were no age differences in compound or single cue – outcome learning, older adults did not retrospectively revalue the causal efficacy of the absent target cues (e.g. STEAK). However, they had weaker within – compound associations for the unrelated foods and this may have prevented them from retrieving the representations of these cues. In Experiment 2, older adults still showed no retrospective revaluation of absent cues even though compound food cues with pre-existing associations were used (e.g., STEAK - POTATO) and they received additional learning trials. Finally, in Experiment 3, older adults revalued the causal efficacy of the target cues when small, unobtrusive icons of these cues were present during single cue revaluation. These findings suggest that age – related deficits in causal learning for absent cues are due to ineffective associative binding and reactivation processes. PMID:21843025
Analyzing brain networks with PCA and conditional Granger causality.

PubMed

Zhou, Zhenyu; Chen, Yonghong; Ding, Mingzhou; Wright, Paul; Lu, Zuhong; Liu, Yijun

2009-07-01

Identifying directional influences in anatomical and functional circuits presents one of the greatest challenges for understanding neural computations in the brain. Granger causality mapping (GCM) derived from vector autoregressive models of data has been employed for this purpose, revealing complex temporal and spatial dynamics underlying cognitive processes. However, the traditional GCM methods are computationally expensive, as signals from thousands of voxels within selected regions of interest (ROIs) are individually processed, and being based on pairwise Granger causality, they lack the ability to distinguish direct from indirect connectivity among brain regions. In this work a new algorithm called PCA based conditional GCM is proposed to overcome these problems. The algorithm implements the following two procedures: (i) dimensionality reduction in ROIs of interest with principle component analysis (PCA), and (ii) estimation of the direct causal influences in local brain networks, using conditional Granger causality. Our results show that the proposed method achieves greater accuracy in detecting network connectivity than the commonly used pairwise Granger causality method. Furthermore, the use of PCA components in conjunction with conditional GCM greatly reduces the computational cost relative to the use of individual voxel time series. Copyright 2009 Wiley-Liss, Inc
Do capuchin monkeys (Cebus apella) diagnose causal relations in the absence of a direct reward?

PubMed

Edwards, Brian J; Rottman, Benjamin M; Shankar, Maya; Betzler, Riana; Chituc, Vladimir; Rodriguez, Ricardo; Silva, Liara; Wibecan, Leah; Widness, Jane; Santos, Laurie R

2014-01-01

We adapted a method from developmental psychology to explore whether capuchin monkeys (Cebus apella) would place objects on a "blicket detector" machine to diagnose causal relations in the absence of a direct reward. Across five experiments, monkeys could place different objects on the machine and obtain evidence about the objects' causal properties based on whether each object "activated" the machine. In Experiments 1-3, monkeys received both audiovisual cues and a food reward whenever the machine activated. In these experiments, monkeys spontaneously placed objects on the machine and succeeded at discriminating various patterns of statistical evidence. In Experiments 4 and 5, we modified the procedure so that in the learning trials, monkeys received the audiovisual cues when the machine activated, but did not receive a food reward. In these experiments, monkeys failed to test novel objects in the absence of an immediate food reward, even when doing so could provide critical information about how to obtain a reward in future test trials in which the food reward delivery device was reattached. The present studies suggest that the gap between human and animal causal cognition may be in part a gap of motivation. Specifically, we propose that monkey causal learning is motivated by the desire to obtain a direct reward, and that unlike humans, monkeys do not engage in learning for learning's sake.
Do Capuchin Monkeys (Cebus apella) Diagnose Causal Relations in the Absence of a Direct Reward?

PubMed Central

Edwards, Brian J.; Rottman, Benjamin M.; Shankar, Maya; Betzler, Riana; Chituc, Vladimir; Rodriguez, Ricardo; Silva, Liara; Wibecan, Leah; Widness, Jane; Santos, Laurie R.

2014-01-01

We adapted a method from developmental psychology [1] to explore whether capuchin monkeys (Cebus apella) would place objects on a “blicket detector” machine to diagnose causal relations in the absence of a direct reward. Across five experiments, monkeys could place different objects on the machine and obtain evidence about the objects’ causal properties based on whether each object “activated” the machine. In Experiments 1–3, monkeys received both audiovisual cues and a food reward whenever the machine activated. In these experiments, monkeys spontaneously placed objects on the machine and succeeded at discriminating various patterns of statistical evidence. In Experiments 4 and 5, we modified the procedure so that in the learning trials, monkeys received the audiovisual cues when the machine activated, but did not receive a food reward. In these experiments, monkeys failed to test novel objects in the absence of an immediate food reward, even when doing so could provide critical information about how to obtain a reward in future test trials in which the food reward delivery device was reattached. The present studies suggest that the gap between human and animal causal cognition may be in part a gap of motivation. Specifically, we propose that monkey causal learning is motivated by the desire to obtain a direct reward, and that unlike humans, monkeys do not engage in learning for learning’s sake. PMID:24586347
The perceived causal structures of smoking: Smoker and non-smoker comparisons

PubMed Central

Lydon, David M; Howard, Matthew C; Wilson, Stephen J; Geier, Charles F

2015-01-01

Despite the detrimental impact of smoking on health, its prevalence remains high. Empirical research has provided insight into the many causes and effects of smoking, yet lay perceptions of smoking remain relatively understudied. The current study used a form of network analysis to gain insight into the causal attributions for smoking of both smoking and non-smoking college students. The analyses resulted in highly endorsed, complex network diagrams that conveyed the perceived causal structures of smoking. Differences in smoker and non-smoker networks emerged with smokers attributing less negative consequences to smoking behaviors. Implications for intervention are discussed. PMID:25690755
Drug-disease association and drug-repositioning predictions in complex diseases using causal inference-probabilistic matrix factorization.

PubMed

Yang, Jihong; Li, Zheng; Fan, Xiaohui; Cheng, Yiyu

2014-09-22

The high incidence of complex diseases has become a worldwide threat to human health. Multiple targets and pathways are perturbed during the pathological process of complex diseases. Systematic investigation of complex relationship between drugs and diseases is necessary for new association discovery and drug repurposing. For this purpose, three causal networks were constructed herein for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. A causal inference-probabilistic matrix factorization (CI-PMF) approach was proposed to predict and classify drug-disease associations, and further used for drug-repositioning predictions. First, multilevel systematic relations between drugs and diseases were integrated from heterogeneous databases to construct causal networks connecting drug-target-pathway-gene-disease. Then, the association scores between drugs and diseases were assessed by evaluating a drug's effects on multiple targets and pathways. Furthermore, PMF models were learned based on known interactions, and associations were then classified into three types by trained models. Finally, therapeutic associations were predicted based upon the ranking of association scores and predicted association types. In terms of drug-disease association prediction, modified causal inference included in CI-PMF outperformed existing causal inference with a higher AUC (area under receiver operating characteristic curve) score and greater precision. Moreover, CI-PMF performed better than single modified causal inference in predicting therapeutic drug-disease associations. In the top 30% of predicted associations, 58.6% (136/232), 50.8% (31/61), and 39.8% (140/352) hit known therapeutic associations, while precisions obtained by the latter were only 10.2% (231/2264), 8.8% (36/411), and 9.7% (189/1948). Clinical verifications were further conducted for the top 100 newly predicted therapeutic associations. As a result, 21, 12, and 32 associations have been studied and many treatment effects of drugs on diseases were investigated for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. Related chains in causal networks were extracted for these 65 clinical-verified associations, and we further illustrated the therapeutic role of etodolac in breast cancer by inferred chains. Overall, CI-PMF is a useful approach for associating drugs with complex diseases and provides potential values for drug repositioning.
Zika Virus Infection and Microcephaly: Evidence for a Causal Link.

PubMed

Wang, Jin-Na; Ling, Feng

2016-10-20

Zika virus (ZIKV) is a flavivirus related to the Dengue, yellow fever and West Nile viruses. Since the explosive outbreaks of ZIKV in Latin America in 2015, a sudden increase in the number of microcephaly cases has been observed in infants of women who were pregnant when they contracted the virus. The severity of this condition raises grave concerns, and extensive studies on the possible link between ZIKV infection and microcephaly have been conducted. There is substantial evidence suggesting that there is a causal link between ZIKV and microcephaly, however, future studies are warranted to solidify this association. To summarize the most recent evidence on this issue and provide perspectives for future studies, we reviewed the literature to identify existing evidence of the causal link between ZIKV infection and microcephaly within research related to the epidemics, laboratory diagnosis, and possible mechanisms.
Causal transfer function analysis to describe closed loop interactions between cardiovascular and cardiorespiratory variability signals.

PubMed

Faes, L; Porta, A; Cucino, R; Cerutti, S; Antolini, R; Nollo, G

2004-06-01

Although the concept of transfer function is intrinsically related to an input-output relationship, the traditional and widely used estimation method merges both feedback and feedforward interactions between the two analyzed signals. This limitation may endanger the reliability of transfer function analysis in biological systems characterized by closed loop interactions. In this study, a method for estimating the transfer function between closed loop interacting signals was proposed and validated in the field of cardiovascular and cardiorespiratory variability. The two analyzed signals x and y were described by a bivariate autoregressive model, and the causal transfer function from x to y was estimated after imposing causality by setting to zero the model coefficients representative of the reverse effects from y to x. The method was tested in simulations reproducing linear open and closed loop interactions, showing a better adherence of the causal transfer function to the theoretical curves with respect to the traditional approach in presence of non-negligible reverse effects. It was then applied in ten healthy young subjects to characterize the transfer functions from respiration to heart period (RR interval) and to systolic arterial pressure (SAP), and from SAP to RR interval. In the first two cases, the causal and non-causal transfer function estimates were comparable, indicating that respiration, acting as exogenous signal, sets an open loop relationship upon SAP and RR interval. On the contrary, causal and traditional transfer functions from SAP to RR were significantly different, suggesting the presence of a considerable influence on the opposite causal direction. Thus, the proposed causal approach seems to be appropriate for the estimation of parameters, like the gain and the phase lag from SAP to RR interval, which have a large clinical and physiological relevance.
Material Phase Causality or a Dynamics-Statistical Interpretation of Quantum Mechanics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Koprinkov, I. G.

2010-11-25

The internal phase dynamics of a quantum system interacting with an electromagnetic field is revealed in details. Theoretical and experimental evidences of a causal relation of the phase of the wave function to the dynamics of the quantum system are presented sistematically for the first time. A dynamics-statistical interpretation of the quantum mechanics is introduced.
A Longitudinal Developmental Analysis of Students' Causality Beliefs about School Performance

ERIC Educational Resources Information Center

Roque, Isabel; de Lemos, Marina Serra; Gonçalves, Teresa

2014-01-01

This study examined the development of school-related causality beliefs which are children's generalized perceptions of the utility or power of different categories of specific means in producing school outcomes. Based on the action theory perspective, we analyzed the developmental model of these beliefs as well as the trajectories of the…
A Storybook Method for Exploring Young Children's Views of Illness Causality in Relation to the Familial Context

ERIC Educational Resources Information Center

McIntosh, Caroline; Stephens, Christine

2012-01-01

In this paper we describe a method for exploring young children's views of illness causality in social context. Studies of children's conceptualisation of illness have predominantly focused on the nature of children's knowledge rather than locating that knowledge within socio-cultural contexts. Adopting a socio-constructivist perspective we sought…
A Causal-Comparative Study of the Affects of Benchmark Assessments on Middle Grades Science Achievement Scores

ERIC Educational Resources Information Center

Galloway, Melissa Ritchie

2016-01-01

The purpose of this causal comparative study was to test the theory of assessment that relates benchmark assessments to the Georgia middle grades science Criterion Referenced Competency Test (CRCT) percentages, controlling for schools who do not administer benchmark assessments versus schools who do administer benchmark assessments for all middle…
Higher Education and Unemployment: A Cointegration and Causality Analysis of the Case of Turkey

ERIC Educational Resources Information Center

Erdem, Ekrem; Tugcu, Can Tansel

2012-01-01

This article analyses the short and the long-term relations between higher education and unemployment in Turkey for the period 1960-2007. It chooses the recently developed ARDL cointegration and Granger causality of Dolado and Lutkepohl (1996) methods. While the proxy of unemployment is total unemployment rate, higher education graduates were…
The Causal Effect of Education on Health: Evidence from the United Kingdom

ERIC Educational Resources Information Center

Silles, Mary A.

2009-01-01

Numerous economic studies have shown a strong positive correlation between health and years of schooling. The question at the centre of this research is whether the correlation between health and education represents a causal relation. This paper uses changes in compulsory schooling laws in the United Kingdom to test this hypothesis. Multiple…
How causal analysis can reveal autonomy in models of biological systems

NASA Astrophysics Data System (ADS)

Marshall, William; Kim, Hyunju; Walker, Sara I.; Tononi, Giulio; Albantakis, Larissa

2017-11-01

Standard techniques for studying biological systems largely focus on their dynamical or, more recently, their informational properties, usually taking either a reductionist or holistic perspective. Yet, studying only individual system elements or the dynamics of the system as a whole disregards the organizational structure of the system-whether there are subsets of elements with joint causes or effects, and whether the system is strongly integrated or composed of several loosely interacting components. Integrated information theory offers a theoretical framework to (1) investigate the compositional cause-effect structure of a system and to (2) identify causal borders of highly integrated elements comprising local maxima of intrinsic cause-effect power. Here we apply this comprehensive causal analysis to a Boolean network model of the fission yeast (Schizosaccharomyces pombe) cell cycle. We demonstrate that this biological model features a non-trivial causal architecture, whose discovery may provide insights about the real cell cycle that could not be gained from holistic or reductionist approaches. We also show how some specific properties of this underlying causal architecture relate to the biological notion of autonomy. Ultimately, we suggest that analysing the causal organization of a system, including key features like intrinsic control and stable causal borders, should prove relevant for distinguishing life from non-life, and thus could also illuminate the origin of life problem. This article is part of the themed issue 'Reconceptualizing the origins of life'.

What Women Think: Cancer Causal Attributions in a Diverse Sample of Women

PubMed Central

Rodríguez, Vivian M.; Gyure, Maria E.; Corona, Rosalie; Bodurtha, Joann N.; Bowen, Deborah J.; Quillin, John M.

2014-01-01

Women hold diverse beliefs about cancer etiology, potentially affecting their use of cancer preventive behaviors. To date, research has greatly focused on the causal attributions cancer patients and survivors hold about cancer, and studies have been conducted primarily with White participants. Less is known about causal attributions held by women with and without a family history of cancer from a diverse community sample. This study sought to identify cancer causal attributions of women with and without a family history of cancer, and explore its relation to socio-cultural factors. Diverse women (60% African-American) recruited at an urban, safety-net women's health clinic (N=471) reported factors they believed cause cancer. Responses were coded into nine attributions and analyzed using chi-squares and logistic regressions. Lifestyle-choices (63%), genetics/heredity (34%), and environmental-exposures (19%) were the top causal attributions identified. Women without a family history of cancer were more likely to identify genetics/heredity as an attribution for cancer than women with a history of cancer in their families. Women who identified as White, who had a higher educational attainment, and had commercial insurance were more likely to report genetics/heredity as a causal attribution for cancer. These findings suggest that socio-cultural factors may play a role in the causal attributions individuals make about cancer, which can, in turn, inform cancer awareness and prevention messages. PMID:25398057
Temperature, Not Fine Particulate Matter (PM2.5), is Causally Associated with Short-Term Acute Daily Mortality Rates: Results from One Hundred United States Cities

PubMed Central

Cox, Tony; Popken, Douglas; Ricci, Paolo F

2013-01-01

Exposures to fine particulate matter (PM2.5) in air (C) have been suspected of contributing causally to increased acute (e.g., same-day or next-day) human mortality rates (R). We tested this causal hypothesis in 100 United States cities using the publicly available NMMAPS database. Although a significant, approximately linear, statistical C-R association exists in simple statistical models, closer analysis suggests that it is not causal. Surprisingly, conditioning on other variables that have been extensively considered in previous analyses (usually using splines or other smoothers to approximate their effects), such as month of the year and mean daily temperature, suggests that they create strong, nonlinear confounding that explains the statistical association between PM2.5 and mortality rates in this data set. As this finding disagrees with conventional wisdom, we apply several different techniques to examine it. Conditional independence tests for potential causation, non-parametric classification tree analysis, Bayesian Model Averaging (BMA), and Granger-Sims causality testing, show no evidence that PM2.5 concentrations have any causal impact on increasing mortality rates. This apparent absence of a causal C-R relation, despite their statistical association, has potentially important implications for managing and communicating the uncertain health risks associated with, but not necessarily caused by, PM2.5 exposures. PMID:23983662
Overcoming confirmation bias in causal attribution: a case study of antibiotic resistance risks.

PubMed

Cox, Louis Anthony Tony; Popken, Douglas A

2008-10-01

When they do not use formal quantitative risk assessment methods, many scientists (like other people) make mistakes and exhibit biases in reasoning about causation, if-then relations, and evidence. Decision-related conclusions or causal explanations are reached prematurely based on narrative plausibility rather than adequate factual evidence. Then, confirming evidence is sought and emphasized, but disconfirming evidence is ignored or discounted. This tendency has serious implications for health-related public policy discussions and decisions. We provide examples occurring in antimicrobial health risk assessments, including a case study of a recently reported positive relation between virginiamycin (VM) use in poultry and risk of resistance to VM-like (streptogramin) antibiotics in humans. This finding has been used to argue that poultry consumption causes increased resistance risks, that serious health impacts may result, and therefore use of VM in poultry should be restricted. However, the original study compared healthy vegetarians to hospitalized poultry consumers. Our examination of the same data using conditional independence tests for potential causality reveals that poultry consumption acted as a surrogate for hospitalization in this study. After accounting for current hospitalization status, no evidence remains supporting a causal relationship between poultry consumption and increased streptogramin resistance. This example emphasizes both the importance and the practical possibility of analyzing and presenting quantitative risk information using data analysis techniques (such as Bayesian model averaging (BMA) and conditional independence tests) that are as free as possible from potential selection, confirmation, and modeling biases.
The latent causal chain of industrial water pollution in China.

PubMed

Miao, Xin; Tang, Yanhong; Wong, Christina W Y; Zang, Hongyu

2015-01-01

The purpose of this paper is to discover the latent causal chain of industrial water pollution in China and find ways to cure the want on discharge of toxic waste from industries. It draws evidences from the past pollution incidents in China. Through further digging the back interests and relations by analyzing representative cases, extended theory about loophole derivations and causal chain effect is drawn. This theoretical breakthrough reflects deeper causality. Institutional defect instead of human error is confirmed as the deeper reason of frequent outbreaks of water pollution incidents in China. Ways for collaborative environmental governance are proposed. This paper contributes to a better understanding about the deep inducements of industrial water pollution in China, and, is meaningful for ensuring future prevention and mitigation of environmental pollution. It illuminates multiple dimensions for collaborative environmental governance to cure the stubborn problem.
Universal behavior of generalized causal set d’Alembertians in curved spacetime

NASA Astrophysics Data System (ADS)

Belenchia, Alessio

2016-07-01

Causal set non-local wave operators allow both for the definition of an action for causal set theory and the study of deviations from local physics that may have interesting phenomenological consequences. It was previously shown that, in all dimensions, the (unique) minimal discrete operators give averaged continuum non-local operators that reduce to \\square -R/2 in the local limit. Recently, dropping the constraint of minimality, it was shown that there exist an infinite number of discrete operators satisfying basic physical requirements and with the right local limit in flat spacetime. In this work, we consider this entire class of generalized causal set d’Alembertins in curved spacetimes and extend to them the result about the universality of the -R/2 factor. Finally, we comment on the relation of this result to the Einstein equivalence principle.
A short educational intervention diminishes causal illusions and specific paranormal beliefs in undergraduates.

PubMed

Barberia, Itxaso; Tubau, Elisabet; Matute, Helena; Rodríguez-Ferreiro, Javier

2018-01-01

Cognitive biases such as causal illusions have been related to paranormal and pseudoscientific beliefs and, thus, pose a real threat to the development of adequate critical thinking abilities. We aimed to reduce causal illusions in undergraduates by means of an educational intervention combining training-in-bias and training-in-rules techniques. First, participants directly experienced situations that tend to induce the Barnum effect and the confirmation bias. Thereafter, these effects were explained and examples of their influence over everyday life were provided. Compared to a control group, participants who received the intervention showed diminished causal illusions in a contingency learning task and a decrease in the precognition dimension of a paranormal belief scale. Overall, results suggest that evidence-based educational interventions like the one presented here could be used to significantly improve critical thinking skills in our students.
Causation in risk assessment and management: models, inference, biases, and a microbial risk-benefit case study.

PubMed

Cox, L A; Ricci, P F

2005-04-01

Causal inference of exposure-response relations from data is a challenging aspect of risk assessment with important implications for public and private risk management. Such inference, which is fundamentally empirical and based on exposure (or dose)-response models, seldom arises from a single set of data; rather, it requires integrating heterogeneous information from diverse sources and disciplines including epidemiology, toxicology, and cell and molecular biology. The causal aspects we discuss focus on these three aspects: drawing sound inferences about causal relations from one or more observational studies; addressing and resolving biases that can affect a single multivariate empirical exposure-response study; and applying the results from these considerations to the microbiological risk management of human health risks and benefits of a ban on antibiotic use in animals, in the context of banning enrofloxacin or macrolides, antibiotics used against bacterial illnesses in poultry, and the effects of such bans on changing the risk of human food-borne campylobacteriosis infections. The purposes of this paper are to describe novel causal methods for assessing empirical causation and inference; exemplify how to deal with biases that routinely arise in multivariate exposure- or dose-response modeling; and provide a simplified discussion of a case study of causal inference using microbial risk analysis as an example. The case study supports the conclusion that the human health benefits from a ban are unlikely to be greater than the excess human health risks that it could create, even when accounting for uncertainty. We conclude that quantitative causal analysis of risks is a preferable to qualitative assessments because it does not involve unjustified loss of information and is sound under the inferential use of risk results by management.
The relationship between awareness of intellectual disability, causal and intervention beliefs and social distance in Kuwait and the UK.

PubMed

Scior, Katrina; Hamid, Aseel; Mahfoudhi, Abdessatar; Abdalla, Fauzia

2013-11-01

Evidence on lay beliefs and stigma associated with intellectual disability in an Arab context is almost non-existent. This study examined awareness of intellectual disability, causal and intervention beliefs and social distance in Kuwait. These were compared to a UK sample to examine differences in lay conceptions across cultures. 537 university students in Kuwait and 571 students in the UK completed a web-based survey asking them to respond to a diagnostically unlabelled vignette of a man presenting with symptoms of mild intellectual disability. They rated their agreement with 22 causal items as possible causes for the difficulties depicted in the vignette, the perceived helpfulness of 22 interventions, and four social distance items using a 7-point Likert scale. Only 8% of Kuwait students, yet 33% of UK students identified possible intellectual disability in the vignette. Medium to large differences between the two samples were observed on seven of the causal items, and 10 of the intervention items. Against predictions, social distance did not differ. Causal beliefs mediated the relationship between recognition of intellectual disability and social distance, but their mediating role differed by sample. The findings are discussed in relation to cultural practices and values, and in relation to attribution theory. In view of the apparent positive effect of awareness of the symptoms of intellectual disability on social distance, both directly and through the mediating effects of causal beliefs, promoting increased awareness of intellectual disability and inclusive practices should be a priority, particularly in countries such as Kuwait where it appears to be low. Copyright © 2013 Elsevier Ltd. All rights reserved.
Locality, reflection, and wave-particle duality

NASA Astrophysics Data System (ADS)

Mugur-Schächter, Mioara

1987-08-01

Bell's theorem is believed to establish that the quantum mechanical predictions do not generally admit a causal representation compatible with Einsten's principle of separability, thereby proving incompatibility between quantum mechanics and relativity. This interpretation is contested via two convergent approaches which lead to a sharp distinction between quantum nonseparability and violation of Einstein's theory of relativity. In a first approach we explicate from the quantum mechanical formalism a concept of “reflected dependence.” Founded on this concept, we produce a causal representation of the quantum mechanical probability measure involved in Bell's proof, which is clearly separable in Einstein's sense, i.e., it does not involve supraluminal velocities, and nevertheless is “nonlocal” in Bell's sense. So Bell locality and Einstein separability are distinct qualifications, and Bell nonlocality (or Bell nonseparability) and Einstein separability are not incompatible. It is then proved explicitly that with respect to the mentioned representation Bell's derivation does not hold. So Bell's derivation does not establish that any Einstein-separable representation is incompatible with quantum mechanics. This first—negative—conclusion is a syntactic fact. The characteristics of the representation and of the reasoning involved in the mentioned counterexample to the usual interpretation of Bell's theorem suggest that the representation used—notwithstanding its ability to bring forth the specified syntactic fact—is not factually true. Factual truth and syntactic properties also have to be radically distinguished in their turn. So, in a second approach, starting from de Broglie's initial relativistic model of a microsystem, a deeper, factually acceptable representation is constructed. The analyses leading to this second representation show that quantum mechanics does indeed involve basically a certain sort of nonseparability, called here de Broglie-Bohr quantum nonseparability. But the de Broglie-Bohr quantum nonseparability is shown to stem directly from the relativistic character of the considerations which led Louis de Broglie to the fundamental relation p = h/λ, thereby being essentially consistent with relativity. As to Einstein separability, it appears to be a still insufficiently specified concept of which a future, improved specification, will probably be explicitly harmonizable with the de Broglie-Bohr quantum nonseparability. The ensemble of the conclusions obtained here brings forth a new concept of causality, a concept of folded, zigzag, reflexive causality, with respect to which the type of causality conceived of up to now appears as a particular case of outstretched, one-way causality. The reflexive causality is found compatible with the results of Aspect's experiment, and it suggests new experiments. Considered globally, the conclusions obtained in the present work might convert the conceptual situation created by Bell's proof into a process of unification of quantum mechanics and relativity.
[Medical expert assessment, objectivity and justice in disability pension cases. ].

PubMed

Solli, Hans Magnus

2003-08-14

The formal principle of justice is often interpreted as the requirement of objectivity when a person's situation is to be evaluated in the light of social justice. The aim of this article is to analyse whether or not the formal principle of justice is fulfilled by the ontological and the epistemological concept of objectivity when disability claims are evaluated medically in relation to the Norwegian legislation on disability benefits. material is legal and medical texts about medical disability evaluation. The method is text analysis. The main result is that the concept of ontological objectivity functions as the criterion of objectivity when the causal relations between sickness, impairment and disability are explained. This criterion is, however, problematic because it is based on the assumption that there is a linear causal model of these relations, which precludes the explanation of many cases of disability. The ontological concept of objectivity is not a necessary condition for impartiality and formal justice in relation to the causal relation between sickness and disability. In some situations this concept is a sufficient condition. The epistemological concept of objectivity is a sufficient condition, but it is not a necessary condition. Some cases must be reviewed on a discretionary basis.
Causal relations among events and states in dynamic geographical phenomena

NASA Astrophysics Data System (ADS)

Huang, Zhaoqiang; Feng, Xuezhi; Xuan, Wenling; Chen, Xiuwan

2007-06-01

There is only a static state of the real world to be recorded in conventional geographical information systems. However, there is not only static information but also dynamic information in geographical phenomena. So that how to record the dynamic information and reveal the relations among dynamic information is an important issue in a spatio-temporal information system. From an ontological perspective, we can initially divide the spatio-temporal entities in the world into continuants and occurrents. Continuant entities endure through some extended (although possibly very short) interval of time (e.g., houses, roads, cities, and real-estate). Occurrent entities happen and are then gone (e.g., a house repair job, road construction project, urban expansion, real-estate transition). From an information system perspective, continuants and occurrents that have a unique identity in the system are referred to as objects and events, respectively. And the change is represented implicitly by static snapshots in current spatial temporal information systems. In the previous models, the objects can be considered as the fundamental components of the system, and the change is modeled by considering time-varying attributes of these objects. In the spatio-temporal database, the temporal information that is either interval or instant is involved and the underlying data structures and indexes for temporal are considerable investigated. However, there is the absence of explicit ways of considering events, which affect the attributes of objects or the state. So the research issue of this paper focuses on how to model events in conceptual models of dynamic geographical phenomena and how to represent the causal relations among events and the objects or states. Firstly, the paper reviews the conceptual modeling in a temporal GIS by researchers. Secondly, this paper discusses the spatio-temporal entities: objects and events. Thirdly, this paper investigates the causal relations amongst events and states. The qualitative spatiotemporal change is an important issue in the dynamic geographic-scale phenomena. In real estate transition, the events and states are needed to be represented explicitly. In our modeling the evolution of a dynamic system, it can not avoid fetching in the view of causality. The object's transition is represented by the state of object. Event causes the state of objects changing and causes other events happen. Events connect with objects closely. The basic causal relations are the state-event and event-state relationships. Lastly, the paper concludes with the overview about the causal relations amongst events and states. And this future work is pointed.
Brain Networks Shaping Religious Belief

PubMed Central

Deshpande, Gopikrishna; Krueger, Frank; Thornburg, Matthew P.; Grafman, Jordan Henry

2014-01-01

Abstract We previously demonstrated with functional magnetic resonance imaging (fMRI) that religious belief depends upon three cognitive dimensions, which can be mapped to specific brain regions. In the present study, we considered these co-activated regions as nodes of three networks each one corresponding to a particular dimension, corresponding to each dimension and examined the causal flow within and between these networks to address two important hypotheses that remained untested in our previous work. First, we hypothesized that regions involved in theory of mind (ToM) are located upstream the causal flow and drive non-ToM regions, in line with theories attributing religion to the evolution of ToM. Second, we hypothesized that differences in directional connectivity are associated with differences in religiosity. To test these hypotheses, we performed a multivariate Granger causality-based directional connectivity analysis of fMRI data to demonstrate the causal flow within religious belief-related networks. Our results supported both hypotheses. Religious subjects preferentially activated a pathway from inferolateral to dorsomedial frontal cortex to monitor the intent and involvement of supernatural agents (SAs; intent-related ToM). Perception of SAs engaged pathways involved in fear regulation and affective ToM. Religious beliefs are founded both on propositional statements for doctrine, but also on episodic memory and imagery. Beliefs based on doctrine engaged a pathway from Broca's to Wernicke's language areas. Beliefs related to everyday life experiences engaged pathways involved in imagery. Beliefs implying less involved SAs and evoking imagery activated a pathway from right lateral temporal to occipital regions. This pathway was more active in non-religious compared to religious subjects, suggesting greater difficulty and procedural demands for imagining and processing the intent of SAs. Insights gained by Granger connectivity analysis inform us about the causal binding of individual regions activated during religious belief processing. PMID:24279687
Brain networks shaping religious belief.

PubMed

Kapogiannis, Dimitrios; Deshpande, Gopikrishna; Krueger, Frank; Thornburg, Matthew P; Grafman, Jordan Henry

2014-02-01

We previously demonstrated with functional magnetic resonance imaging (fMRI) that religious belief depends upon three cognitive dimensions, which can be mapped to specific brain regions. In the present study, we considered these co-activated regions as nodes of three networks each one corresponding to a particular dimension, corresponding to each dimension and examined the causal flow within and between these networks to address two important hypotheses that remained untested in our previous work. First, we hypothesized that regions involved in theory of mind (ToM) are located upstream the causal flow and drive non-ToM regions, in line with theories attributing religion to the evolution of ToM. Second, we hypothesized that differences in directional connectivity are associated with differences in religiosity. To test these hypotheses, we performed a multivariate Granger causality-based directional connectivity analysis of fMRI data to demonstrate the causal flow within religious belief-related networks. Our results supported both hypotheses. Religious subjects preferentially activated a pathway from inferolateral to dorsomedial frontal cortex to monitor the intent and involvement of supernatural agents (SAs; intent-related ToM). Perception of SAs engaged pathways involved in fear regulation and affective ToM. Religious beliefs are founded both on propositional statements for doctrine, but also on episodic memory and imagery. Beliefs based on doctrine engaged a pathway from Broca's to Wernicke's language areas. Beliefs related to everyday life experiences engaged pathways involved in imagery. Beliefs implying less involved SAs and evoking imagery activated a pathway from right lateral temporal to occipital regions. This pathway was more active in non-religious compared to religious subjects, suggesting greater difficulty and procedural demands for imagining and processing the intent of SAs. Insights gained by Granger connectivity analysis inform us about the causal binding of individual regions activated during religious belief processing.
Development of Effective Connectivity during Own- and Other-Race Face Processing: A Granger Causality Analysis

PubMed Central

Zhou, Guifei; Liu, Jiangang; Ding, Xiao Pan; Fu, Genyue; Lee, Kang

2016-01-01

Numerous developmental studies have suggested that other-race effect (ORE) in face recognition emerges as early as in infancy and develops steadily throughout childhood. However, there is very limited research on the neural mechanisms underlying this developmental ORE. The present study used Granger causality analysis (GCA) to examine the development of children's cortical networks in processing own- and other-race faces. Children were between 3 and 13 years. An old-new paradigm was used to assess their own- and other-race face recognition with ETG-4000 (Hitachi Medical Co., Japan) acquiring functional near infrared spectroscopy (fNIRS) data. After preprocessing, for each participant and under each face condition, we obtained the causal map by calculating the weights of causal relations between the time courses of [oxy-Hb] of each pair of channels using GCA. To investigate further the differential causal connectivity for own-race faces and other-race faces at the group level, a repeated measure analysis of variance (ANOVA) was performed on the GCA weights for each pair of channels with the face race task (own-race face vs. other-race face) as the within-subject variable and the age as a between-subject factor (continuous variable). We found an age-related increase in functional connectivity, paralleling a similar age-related improvement in behavioral face processing ability. More importantly, we found that the significant differences in neural functional connectivity between the recognition of own-race faces and that of other-race faces were modulated by age. Thus, like the behavioral ORE, the neural ORE emerges early and undergoes a protracted developmental course. PMID:27713696
Causal Inference in Educational Effectiveness Research: A Comparison of Three Methods to Investigate Effects of Homework on Student Achievement

ERIC Educational Resources Information Center

Gustafsson, Jan-Eric

2013-01-01

In educational effectiveness research, it frequently has proven difficult to make credible inferences about cause and effect relations. The article first identifies the main categories of threats to valid causal inference from observational data, and discusses designs and analytic approaches which protect against them. With the use of data from 22…
Relationships between Adolescents' Preferred Sources of Help and Emotional Distress, Ambivalence over Emotional Expression, and Causal Attribution of Symptoms: A Singapore Study

ERIC Educational Resources Information Center

Lee, Boon-Ooi

2009-01-01

Past research has shown that many adolescents with depression and anxiety disorders do not consult mental health professionals. This study examines how emotional distress, ambivalence over emotional expression, and causal attribution of depressive and anxious symptoms are related to adolescents' preferred sources of help for these symptoms. 300…
Teachers' Interpersonal Style and Its Relationship to Emotions, Causal Attributions, and Type of Challenging Behaviors Displayed by Students with Intellectual Disabilities

ERIC Educational Resources Information Center

Alevriadou, Anastasia; Pavlidou, Kyriaki

2016-01-01

Teachers' interpersonal style is a new field of research in the study of students with intellectual disabilities and challenging behaviors in school context. In the present study, we investigate emotions and causal attributions of three basic types of challenging behaviors: aggression, stereotypy, and self-injury, in relation to teachers'…
Non-linear Interactions between Niño region 3 and the Southern Amazon

NASA Astrophysics Data System (ADS)

Ramos, A. M. D. T.; Builes-Jaramillo, L. A.; Poveda, G.; Goswami, B.; Macau, E. E. N.; Kurths, J.; Marwan, N.

2016-12-01

Identifying causal relations from the observational dataset has posed great challenges in data-driven inference study. However, complex system framework offers promising approaches to tackle such problems. Here we propose a new data-driven causality inference method using the framework of recurrence plots. We present the Recurrence Measure of Conditional Dependence (RMCD) and its applications. The RMCD incorporates the recurrence behavior into the transfer entropy theory. Therefore, it quantifies the causal dependence between two processes based on joint recurrence patterns between the past of the potential driver and present on the potential driven, except for any contribution that has already been in the past of the driven. We apply this methodology to some paradigmatic models and to investigate the possible influence of the Pacific Ocean temperatures on the South West Amazon for the 2010 and 2005 droughts. The results reveal that for the 2005 drought there is not a significant signal of dependence from the Pacific Ocean and that for 2010 there is a signal of dependence of around 200 days. These outcomes are confirmed by the traditional climatological analysis of these episodes available in the literature and show the accuracy of RMCD inferring causal relations in climate systems.
Obesity and infection: reciprocal causality.

PubMed

Hainer, V; Zamrazilová, H; Kunešová, M; Bendlová, B; Aldhoon-Hainerová, I

2015-01-01

Associations between different infectious agents and obesity have been reported in humans for over thirty years. In many cases, as in nosocomial infections, this relationship reflects the greater susceptibility of obese individuals to infection due to impaired immunity. In such cases, the infection is not related to obesity as a causal factor but represents a complication of obesity. In contrast, several infections have been suggested as potential causal factors in human obesity. However, evidence of a causal linkage to human obesity has only been provided for adenovirus 36 (Adv36). This virus activates lipogenic and proinflammatory pathways in adipose tissue, improves insulin sensitivity, lipid profile and hepatic steatosis. The E4orf1 gene of Adv36 exerts insulin senzitizing effects, but is devoid of its pro-inflammatory modalities. The development of a vaccine to prevent Adv36-induced obesity or the use of E4orf1 as a ligand for novel antidiabetic drugs could open new horizons in the prophylaxis and treatment of obesity and diabetes. More experimental and clinical studies are needed to elucidate the mutual relations between infection and obesity, identify additional infectious agents causing human obesity, as well as define the conditions that predispose obese individuals to specific infections.
Intolerance of uncertainty, causal uncertainty, causal importance, self-concept clarity and their relations to generalized anxiety disorder.

PubMed

Kusec, Andrea; Tallon, Kathleen; Koerner, Naomi

2016-06-01

Although numerous studies have provided support for the notion that intolerance of uncertainty plays a key role in pathological worry (the hallmark feature of generalized anxiety disorder (GAD)), other uncertainty-related constructs may also have relevance for the understanding of individuals who engage in pathological worry. Three constructs from the social cognition literature, causal uncertainty, causal importance, and self-concept clarity, were examined in the present study to assess the degree to which these explain unique variance in GAD, over and above intolerance of uncertainty. N = 235 participants completed self-report measures of trait worry, GAD symptoms, and uncertainty-relevant constructs. A subgroup was subsequently classified as low in GAD symptoms (n = 69) or high in GAD symptoms (n = 54) based on validated cut scores on measures of trait worry and GAD symptoms. In logistic regressions, only elevated intolerance of uncertainty and lower self-concept clarity emerged as unique correlates of high (vs. low) GAD symptoms. The possible role of self-concept uncertainty in GAD and the utility of integrating social cognition theories and constructs into clinical research on intolerance of uncertainty are discussed.

What Is Going on Inside the Arrows? Discovering the Hidden Springs in Causal Models

PubMed Central

Murray-Watters, Alexander; Glymour, Clark

2016-01-01

Using Gebharter's (2014) representation, we consider aspects of the problem of discovering the structure of unmeasured sub-mechanisms when the variables in those sub-mechanisms have not been measured. Exploiting an early insight of Sober's (1998), we provide a correct algorithm for identifying latent, endogenous structure—sub-mechanisms—for a restricted class of structures. The algorithm can be merged with other methods for discovering causal relations among unmeasured variables, and feedback relations between measured variables and unobserved causes can sometimes be learned. PMID:27313331
Etiology of depression comorbidity in combat-related PTSD: a review of the literature.

PubMed

Stander, Valerie A; Thomsen, Cynthia J; Highfill-McRoy, Robyn M

2014-03-01

Posttraumatic stress disorder is often diagnosed with other mental health problems, particularly depression. Although PTSD comorbidity has been associated with more severe and chronic symptomology, relationships among commonly co-occurring disorders are not well understood. The purpose of this study was to review the literature regarding the development of depression comorbid with combat-related PTSD among military personnel. We summarize results of commonly tested hypotheses about the etiology of PTSD and depression comorbidity, including (1) causal hypotheses, (2) common factor hypotheses, and (3) potential confounds. Evidence suggests that PTSD may be a causal risk factor for subsequent depression; however, associations are likely complex, involving bidirectional causality, common risk factors, and common vulnerabilities. The unique nature of PTSD-depression comorbidity in the context of military deployment and combat exposure is emphasized. Implications of our results for clinical practice and future research are discussed. Published by Elsevier Ltd.
Reward-Guided Learning with and without Causal Attribution

PubMed Central

Jocham, Gerhard; Brodersen, Kay H.; Constantinescu, Alexandra O.; Kahn, Martin C.; Ianni, Angela M.; Walton, Mark E.; Rushworth, Matthew F.S.; Behrens, Timothy E.J.

2016-01-01

Summary When an organism receives a reward, it is crucial to know which of many candidate actions caused this reward. However, recent work suggests that learning is possible even when this most fundamental assumption is not met. We used novel reward-guided learning paradigms in two fMRI studies to show that humans deploy separable learning mechanisms that operate in parallel. While behavior was dominated by precise contingent learning, it also revealed hallmarks of noncontingent learning strategies. These learning mechanisms were separable behaviorally and neurally. Lateral orbitofrontal cortex supported contingent learning and reflected contingencies between outcomes and their causal choices. Amygdala responses around reward times related to statistical patterns of learning. Time-based heuristic mechanisms were related to activity in sensorimotor corticostriatal circuitry. Our data point to the existence of several learning mechanisms in the human brain, of which only one relies on applying known rules about the causal structure of the task. PMID:26971947
Genomic Influences on Hyperuricemia and Gout.

PubMed

Merriman, Tony

2017-08-01

Genome-wide association studies (GWAS) have identified nearly 30 loci associated with urate concentrations that also influence the subsequent risk of gout. The ABCG2 Q141 K variant is highly likely to be causal and results in internalization of ABCG2, which can be rescued by drugs. Three other GWAS loci contain uric acid transporter genes, which are also highly likely to be causal. However identification of causal genes at other urate loci is challenging. Finally, relatively little is known about the genetic control of progression from hyperuricemia to gout. Only 4 small GWAS have been published for gout. Copyright © 2017 Elsevier Inc. All rights reserved.
Genetically determined schizophrenia is not associated with impaired glucose homeostasis.

PubMed

Polimanti, Renato; Gelernter, Joel; Stein, Dan J

2018-05-01

Here, we used data from large genome-wide association studies to test the presence of causal relationships, conducting a Mendelian randomization analysis; and shared molecular mechanisms, calculating the genetic correlation, among schizophrenia, type 2 diabetes (T2D), and impaired glucose homeostasis. Although our Mendelian randomization analysis was well-powered, no causal relationship was observed between schizophrenia and T2D, or traits related to glucose impaired homeostasis. Similarly, we did not observe any global genetic overlap among these traits. These findings indicate that there is no causal relationships or shared mechanisms between schizophrenia and impaired glucose homeostasis. Copyright © 2017 Elsevier B.V. All rights reserved.
Causal cognition in human and nonhuman animals: a comparative, critical review.

PubMed

Penn, Derek C; Povinelli, Daniel J

2007-01-01

In this article, we review some of the most provocative experimental results to have emerged from comparative labs in the past few years, starting with research focusing on contingency learning and finishing with experiments exploring nonhuman animals' understanding of causal-logical relations. Although the theoretical explanation for these results is often inchoate, a clear pattern nevertheless emerges. The comparative evidence does not fit comfortably into either the traditional associationist or inferential alternatives that have dominated comparative debate for many decades now. Indeed, the similarities and differences between human and nonhuman causal cognition seem to be much more multifarious than these dichotomous alternatives allow.
Analogical and category-based inference: a theoretical integration with Bayesian causal models.

PubMed

Holyoak, Keith J; Lee, Hee Seung; Lu, Hongjing

2010-11-01

A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source information at various levels of abstraction (including both specific instances and more general categories), coupled with prior causal knowledge, to build a causal model for a target situation, which in turn constrains inferences about the target. We propose a computational theory in the framework of Bayesian inference and test its predictions (parameter-free for the cases we consider) in a series of experiments in which people were asked to assess the probabilities of various causal predictions and attributions about a target on the basis of source knowledge about generative and preventive causes. The theory proved successful in accounting for systematic patterns of judgments about interrelated types of causal inferences, including evidence that analogical inferences are partially dissociable from overall mapping quality.
Strengthening of causality assessment of adverse events following immunization in the WHO South East Asia and Western Pacific regions: Lessons from the 2014 SEAR inter-country workshop.

PubMed

MacDonald, Noni E; Guichard, Stephane; Amarasinghe, Ananda; Balakrishnan, Madhava Ram

2015-11-27

Poorly managed AEFI undermine immunization programs. Improved surveillance in SEAR countries means more AEFIs but management varies. SEAR brought countries together to share AEFI experiences, and learn more about causality assessment. Three day 10 country workshop (9 SEAR; 1 WPR). Participants outlined county AEFI experiences, undertook causality assessment for 8 AEFIs using WHO methodology, critiqued the process by questionnaire and had a discussion. All 10 valued AEFI monitoring and causality assessment, and praised the opportunity to share experiences. Participants determined a range of AEFI and causality assessment needs in SEAR such as adapting WHO Algorithm, CIOMS/Brighton definitions, WHO verbal autopsy to fit context, requesting a practical guide--AEFI definition, time interval, rates of AEFI for different vaccines and evidence for vaccine related causes of death under 24h. LMIC need WHO AEFI tools adapted to better fit LMIC. Learning from each other builds capacity. Sharing AEFI experiences, case reviews help LMIC improve practices. Copyright © 2015. Published by Elsevier Ltd.
Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Fengbin, E-mail: fblu@amss.ac.cn

This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relationsmore » evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.« less
Do New Caledonian crows solve physical problems through causal reasoning?

PubMed Central

Taylor, A.H.; Hunt, G.R.; Medina, F.S.; Gray, R.D.

2008-01-01

The extent to which animals other than humans can reason about physical problems is contentious. The benchmark test for this ability has been the trap-tube task. We presented New Caledonian crows with a series of two-trap versions of this problem. Three out of six crows solved the initial trap-tube. These crows continued to avoid the trap when the arbitrary features that had previously been associated with successful performances were removed. However, they did not avoid the trap when a hole and a functional trap were in the tube. In contrast to a recent primate study, the three crows then solved a causally equivalent but visually distinct problem—the trap-table task. The performance of the three crows across the four transfers made explanations based on chance, associative learning, visual and tactile generalization, and previous dispositions unlikely. Our findings suggest that New Caledonian crows can solve complex physical problems by reasoning both causally and analogically about causal relations. Causal and analogical reasoning may form the basis of the New Caledonian crow's exceptional tool skills. PMID:18796393
Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory.

PubMed

Gopnik, Alison; Wellman, Henry M

2012-11-01

We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and nontechnical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and the psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.
Exposure to traffic-related air pollution during pregnancy and term low birth weight: estimation of causal associations in a semiparametric model.

PubMed

Padula, Amy M; Mortimer, Kathleen; Hubbard, Alan; Lurmann, Frederick; Jerrett, Michael; Tager, Ira B

2012-11-01

Traffic-related air pollution is recognized as an important contributor to health problems. Epidemiologic analyses suggest that prenatal exposure to traffic-related air pollutants may be associated with adverse birth outcomes; however, there is insufficient evidence to conclude that the relation is causal. The Study of Air Pollution, Genetics and Early Life Events comprises all births to women living in 4 counties in California's San Joaquin Valley during the years 2000-2006. The probability of low birth weight among full-term infants in the population was estimated using machine learning and targeted maximum likelihood estimation for each quartile of traffic exposure during pregnancy. If everyone lived near high-volume freeways (approximated as the fourth quartile of traffic density), the estimated probability of term low birth weight would be 2.27% (95% confidence interval: 2.16, 2.38) as compared with 2.02% (95% confidence interval: 1.90, 2.12) if everyone lived near smaller local roads (first quartile of traffic density). Assessment of potentially causal associations, in the absence of arbitrary model assumptions applied to the data, should result in relatively unbiased estimates. The current results support findings from previous studies that prenatal exposure to traffic-related air pollution may adversely affect birth weight among full-term infants.
Formalizing Neurath's ship: Approximate algorithms for online causal learning.

PubMed

Bramley, Neil R; Dayan, Peter; Griffiths, Thomas L; Lagnado, David A

2017-04-01

Higher-level cognition depends on the ability to learn models of the world. We can characterize this at the computational level as a structure-learning problem with the goal of best identifying the prevailing causal relationships among a set of relata. However, the computational cost of performing exact Bayesian inference over causal models grows rapidly as the number of relata increases. This implies that the cognitive processes underlying causal learning must be substantially approximate. A powerful class of approximations that focuses on the sequential absorption of successive inputs is captured by the Neurath's ship metaphor in philosophy of science, where theory change is cast as a stochastic and gradual process shaped as much by people's limited willingness to abandon their current theory when considering alternatives as by the ground truth they hope to approach. Inspired by this metaphor and by algorithms for approximating Bayesian inference in machine learning, we propose an algorithmic-level model of causal structure learning under which learners represent only a single global hypothesis that they update locally as they gather evidence. We propose a related scheme for understanding how, under these limitations, learners choose informative interventions that manipulate the causal system to help elucidate its workings. We find support for our approach in the analysis of 3 experiments. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Beyond Scientism and Skepticism: An Integrative Approach to Global Mental Health

PubMed Central

Stein, Dan J.; Illes, Judy

2015-01-01

The global burden of disorders has shifted from infectious disease to non-communicable diseases, including neuropsychiatric disorders. Whereas infectious disease can sometimes be combated by targeting single causal mechanisms, such as prevention of contact-spread illness by handwashing, in the case of mental disorders multiple causal mechanisms are typically relevant. The emergent field of global mental health has emphasized the magnitude of the treatment gap, particularly in the low- and middle-income world and has paid particular attention to upstream causal factors, for example, poverty, inequality, and gender discrimination in the pathogenesis of mental disorders. However, this field has also been criticized for relying erroneously on Western paradigms of mental illness, which may not be relevant or appropriate to the low- and middle-income context. Here, it is important to steer a path between scientism and skepticism. Scientism regards mental disorders as essential categories, and takes a covering law approach to causality; skepticism regards mental disorders as merely social constructions and emphasizes the role of political power in causal relations. We propose an integrative model that emphasizes the contribution of a broad range of causal mechanisms operating at biological and societal levels to mental disorders and the consequent importance of broad spectrum and multipronged approaches to intervention. PMID:26635641
Safety reports on the off-label use of baclofen for alcohol-dependence: recommendations to improve causality assessment.

PubMed

Rolland, Benjamin; Auffret, Marine; Franchitto, Nicolas

2016-06-01

The off-label use of high-dose baclofen (HDB) for alcohol-dependence has recently spread. However, HDB has been associated with numerous reports of adverse events (AEs). Pharmacovigilance reporting is supposed to differentiate AEs from adverse drug reactions (ADRs), for which the causality of the drug is determined using validated methods. Since 2010, we found 20 publications on baclofen-related AEs in alcohol dependence, in Medline-referenced journals or national pharmacovigilance reports. We focused on whether these reports used causality algorithms, and provided essential elements for determining baclofen causality and excluding the involvement of alcohol and other psychoactive substances or psychotropic drugs. In half of the cases, no causality algorithm was used. Detailed information on baclofen dosing was found in 17 out of 20 (85%) articles, whereas alcohol doses were given only in 10 (50%) publications. Other psychoactive substances and psychotropic drugs were broached in 14 (70%) publications. future publications reporting suspected HDB-induced ADRs should use validated causality algorithms and provide sufficient amount of contextual information for excluding other potential causes. For HDB, the psychiatric history, and the longitudinal description of alcohol consumptions and associated doses of psychoactive substances or psychotropic medications should be detailed for every reported case.
Information flow and causality as rigorous notions ab initio

NASA Astrophysics Data System (ADS)

Liang, X. San

2016-11-01

Information flow or information transfer the widely applicable general physics notion can be rigorously derived from first principles, rather than axiomatically proposed as an ansatz. Its logical association with causality is firmly rooted in the dynamical system that lies beneath. The principle of nil causality that reads, an event is not causal to another if the evolution of the latter is independent of the former, which transfer entropy analysis and Granger causality test fail to verify in many situations, turns out to be a proven theorem here. Established in this study are the information flows among the components of time-discrete mappings and time-continuous dynamical systems, both deterministic and stochastic. They have been obtained explicitly in closed form, and put to applications with the benchmark systems such as the Kaplan-Yorke map, Rössler system, baker transformation, Hénon map, and stochastic potential flow. Besides unraveling the causal relations as expected from the respective systems, some of the applications show that the information flow structure underlying a complex trajectory pattern could be tractable. For linear systems, the resulting remarkably concise formula asserts analytically that causation implies correlation, while correlation does not imply causation, providing a mathematical basis for the long-standing philosophical debate over causation versus correlation.
Causal impulse response for circular sources in viscous media

PubMed Central

Kelly, James F.; McGough, Robert J.

2008-01-01

The causal impulse response of the velocity potential for the Stokes wave equation is derived for calculations of transient velocity potential fields generated by circular pistons in viscous media. The causal Green’s function is numerically verified using the material impulse response function approach. The causal, lossy impulse response for a baffled circular piston is then calculated within the near field and the far field regions using expressions previously derived for the fast near field method. Transient velocity potential fields in viscous media are computed with the causal, lossy impulse response and compared to results obtained with the lossless impulse response. The numerical error in the computed velocity potential field is quantitatively analyzed for a range of viscous relaxation times and piston radii. Results show that the largest errors are generated in locations near the piston face and for large relaxation times, and errors are relatively small otherwise. Unlike previous frequency-domain methods that require numerical inverse Fourier transforms for the evaluation of the lossy impulse response, the present approach calculates the lossy impulse response directly in the time domain. The results indicate that this causal impulse response is ideal for time-domain calculations that simultaneously account for diffraction and quadratic frequency-dependent attenuation in viscous media. PMID:18397018
Framework for assessing causality of air pollution-related health effects for reviews of the National Ambient Air Quality Standards.

PubMed

Owens, Elizabeth Oesterling; Patel, Molini M; Kirrane, Ellen; Long, Thomas C; Brown, James; Cote, Ila; Ross, Mary A; Dutton, Steven J

2017-08-01

To inform regulatory decisions on the risk due to exposure to ambient air pollution, consistent and transparent communication of the scientific evidence is essential. The United States Environmental Protection Agency (U.S. EPA) develops the Integrated Science Assessment (ISA), which contains evaluations of the policy-relevant science on the effects of criteria air pollutants and conveys critical science judgments to inform decisions on the National Ambient Air Quality Standards. This article discusses the approach and causal framework used in the ISAs to evaluate and integrate various lines of scientific evidence and draw conclusions about the causal nature of air pollution-induced health effects. The framework has been applied to diverse pollutants and cancer and noncancer effects. To demonstrate its flexibility, we provide examples of causality judgments on relationships between health effects and pollutant exposures, drawing from recent ISAs for ozone, lead, carbon monoxide, and oxides of nitrogen. U.S. EPA's causal framework has increased transparency by establishing a structured process for evaluating and integrating various lines of evidence and uniform approach for determining causality. The framework brings consistency and specificity to the conclusions in the ISA, and the flexibility of the framework makes it relevant for evaluations of evidence across media and health effects. Published by Elsevier Inc.
Causality and cointegration analysis between macroeconomic variables and the Bovespa.

PubMed

da Silva, Fabiano Mello; Coronel, Daniel Arruda; Vieira, Kelmara Mendes

2014-01-01

The aim of this study is to analyze the causality relationship among a set of macroeconomic variables, represented by the exchange rate, interest rate, inflation (CPI), industrial production index as a proxy for gross domestic product in relation to the index of the São Paulo Stock Exchange (Bovespa). The period of analysis corresponded to the months from January 1995 to December 2010, making a total of 192 observations for each variable. Johansen tests, through the statistics of the trace and of the maximum eigenvalue, indicated the existence of at least one cointegration vector. In the analysis of Granger (1988) causality tests via error correction, it was found that a short-term causality existed between the CPI and the Bovespa. Regarding the Granger (1988) long-term causality, the results indicated a long-term behaviour among the macroeconomic variables with the BOVESPA. The results of the long-term normalized vector for the Bovespa variable showed that most signals of the cointegration equation parameters are in accordance with what is suggested by the economic theory. In other words, there was a positive behaviour of the GDP and a negative behaviour of the inflation and of the exchange rate (expected to be a positive relationship) in relation to the Bovespa, with the exception of the Selic rate, which was not significant with that index. The variance of the Bovespa was explained by itself in over 90% at the twelfth month, followed by the country risk, with less than 5%.
Causality and Cointegration Analysis between Macroeconomic Variables and the Bovespa

PubMed Central

da Silva, Fabiano Mello; Coronel, Daniel Arruda; Vieira, Kelmara Mendes

2014-01-01

The aim of this study is to analyze the causality relationship among a set of macroeconomic variables, represented by the exchange rate, interest rate, inflation (CPI), industrial production index as a proxy for gross domestic product in relation to the index of the São Paulo Stock Exchange (Bovespa). The period of analysis corresponded to the months from January 1995 to December 2010, making a total of 192 observations for each variable. Johansen tests, through the statistics of the trace and of the maximum eigenvalue, indicated the existence of at least one cointegration vector. In the analysis of Granger (1988) causality tests via error correction, it was found that a short-term causality existed between the CPI and the Bovespa. Regarding the Granger (1988) long-term causality, the results indicated a long-term behaviour among the macroeconomic variables with the BOVESPA. The results of the long-term normalized vector for the Bovespa variable showed that most signals of the cointegration equation parameters are in accordance with what is suggested by the economic theory. In other words, there was a positive behaviour of the GDP and a negative behaviour of the inflation and of the exchange rate (expected to be a positive relationship) in relation to the Bovespa, with the exception of the Selic rate, which was not significant with that index. The variance of the Bovespa was explained by itself in over 90% at the twelth month, followed by the country risk, with less than 5%. PMID:24587019

Causality and complexity: the myth of objectivity in science.

PubMed

Mikulecky, Donald C

2007-10-01

Two distinctly different worldviews dominate today's thinking in science and in the world of ideas outside of science. Using the approach advocated by Robert M. Hutchins, it is possible to see a pattern of interaction between ideas in science and in other spheres such as philosophy, religion, and politics. Instead of compartmentalizing these intellectual activities, it is worthwhile to look for common threads of mutual influence. Robert Rosen has created an approach to scientific epistemology that might seem radical to some. However, it has characteristics that resemble ideas in other fields, in particular in the writings of George Lakoff, Leo Strauss, and George Soros. Historically, the atmosphere at the University of Chicago during Hutchins' presidency gave rise to Rashevsky's relational biology, which Rosen carried forward. Strauss was writing his political philosophy there at the same time. One idea is paramount in all this, and it is Lakoff who gives us the most insight into how the worldviews differ using this idea. The central difference has to do with causality, the fundamental concept that we use to build a worldview. Causal entailment has two distinct forms in Lakoff 's analysis: direct causality and complex causality. Rosen's writings on complexity create a picture of complex causality that is extremely useful in its detail, grounding in the ideas of Aristotle. Strauss asks for a return to the ancients to put philosophy back on track. Lakoff sees the weaknesses in Western philosophy in a similar way, and Rosen provides tools for dealing with the problem. This introduction to the relationships between the thinking of these authors is meant to stimulate further discourse on the role of complex causal entailment in all areas of thought, and how it brings them together in a holistic worldview. The worldview built on complex causality is clearly distinct from that built around simple, direct causality. One important difference is that the impoverished causal entailment that accompanies the machine metaphor in science is unable to give us a clear way to distinguish living organisms from machines. Complex causality finds a dichotomy between organisms, which are closed to efficient cause, and machines, which require entailment from outside. An argument can be made that confusing living organisms with machines, as is done in the worldview using direct cause, makes religion a necessity to supply the missing causal entailment.
Genetic Evidence for Causal Relationships Between Maternal Obesity-Related Traits and Birth Weight.

PubMed

Tyrrell, Jessica; Richmond, Rebecca C; Palmer, Tom M; Feenstra, Bjarke; Rangarajan, Janani; Metrustry, Sarah; Cavadino, Alana; Paternoster, Lavinia; Armstrong, Loren L; De Silva, N Maneka G; Wood, Andrew R; Horikoshi, Momoko; Geller, Frank; Myhre, Ronny; Bradfield, Jonathan P; Kreiner-Møller, Eskil; Huikari, Ville; Painter, Jodie N; Hottenga, Jouke-Jan; Allard, Catherine; Berry, Diane J; Bouchard, Luigi; Das, Shikta; Evans, David M; Hakonarson, Hakon; Hayes, M Geoffrey; Heikkinen, Jani; Hofman, Albert; Knight, Bridget; Lind, Penelope A; McCarthy, Mark I; McMahon, George; Medland, Sarah E; Melbye, Mads; Morris, Andrew P; Nodzenski, Michael; Reichetzeder, Christoph; Ring, Susan M; Sebert, Sylvain; Sengpiel, Verena; Sørensen, Thorkild I A; Willemsen, Gonneke; de Geus, Eco J C; Martin, Nicholas G; Spector, Tim D; Power, Christine; Järvelin, Marjo-Riitta; Bisgaard, Hans; Grant, Struan F A; Nohr, Ellen A; Jaddoe, Vincent W; Jacobsson, Bo; Murray, Jeffrey C; Hocher, Berthold; Hattersley, Andrew T; Scholtens, Denise M; Davey Smith, George; Hivert, Marie-France; Felix, Janine F; Hyppönen, Elina; Lowe, William L; Frayling, Timothy M; Lawlor, Debbie A; Freathy, Rachel M

2016-03-15

Neonates born to overweight or obese women are larger and at higher risk of birth complications. Many maternal obesity-related traits are observationally associated with birth weight, but the causal nature of these associations is uncertain. To test for genetic evidence of causal associations of maternal body mass index (BMI) and related traits with birth weight. Mendelian randomization to test whether maternal BMI and obesity-related traits are potentially causally related to offspring birth weight. Data from 30,487 women in 18 studies were analyzed. Participants were of European ancestry from population- or community-based studies in Europe, North America, or Australia and were part of the Early Growth Genetics Consortium. Live, term, singleton offspring born between 1929 and 2013 were included. Genetic scores for BMI, fasting glucose level, type 2 diabetes, systolic blood pressure (SBP), triglyceride level, high-density lipoprotein cholesterol (HDL-C) level, vitamin D status, and adiponectin level. Offspring birth weight from 18 studies. Among the 30,487 newborns the mean birth weight in the various cohorts ranged from 3325 g to 3679 g. The maternal genetic score for BMI was associated with a 2-g (95% CI, 0 to 3 g) higher offspring birth weight per maternal BMI-raising allele (P = .008). The maternal genetic scores for fasting glucose and SBP were also associated with birth weight with effect sizes of 8 g (95% CI, 6 to 10 g) per glucose-raising allele (P = 7 × 10(-14)) and -4 g (95% CI, -6 to -2 g) per SBP-raising allele (P = 1×10(-5)), respectively. A 1-SD ( ≈ 4 points) genetically higher maternal BMI was associated with a 55-g higher offspring birth weight (95% CI, 17 to 93 g). A 1-SD ( ≈ 7.2 mg/dL) genetically higher maternal fasting glucose concentration was associated with 114-g higher offspring birth weight (95% CI, 80 to 147 g). However, a 1-SD ( ≈ 10 mm Hg) genetically higher maternal SBP was associated with a 208-g lower offspring birth weight (95% CI, -394 to -21 g). For BMI and fasting glucose, genetic associations were consistent with the observational associations, but for systolic blood pressure, the genetic and observational associations were in opposite directions. In this mendelian randomization study, genetically elevated maternal BMI and blood glucose levels were potentially causally associated with higher offspring birth weight, whereas genetically elevated maternal SBP was potentially causally related to lower birth weight. If replicated, these findings may have implications for counseling and managing pregnancies to avoid adverse weight-related birth outcomes.
Spectroscopie pompe-sonde pour la détection de bioaérosols

NASA Astrophysics Data System (ADS)

Guyon, L.; Courvoisier, F.; Wood, V.; Boutou, V.; Bartelt, A.; Roth, M.; Rabitz, H.; Wolf, J. P.

2006-10-01

La fluorescence du Tryptophane excité par une impulsion ultra-brève à 270 nm peut être diminuée d'un facteur deux par une seconde impulsion à 800 nm, à l'aide d'un dispositif pompe-sonde. Cette décroissance est aussi observée pour les bactéries vivantes, dont le Tryptophane est l'un des fluorophores, tandis qu'aucune décroissance n'est observée pour d'autres molécules organiques comme le naphtalène ou le gazole, malgré des spectres d'absorption et de fluorescence similaires. Cette différence remarquable est très prometteuse pour la distinction d'aérosols biologiques et organiques.
A short educational intervention diminishes causal illusions and specific paranormal beliefs in undergraduates

PubMed Central

Tubau, Elisabet; Matute, Helena

2018-01-01

Cognitive biases such as causal illusions have been related to paranormal and pseudoscientific beliefs and, thus, pose a real threat to the development of adequate critical thinking abilities. We aimed to reduce causal illusions in undergraduates by means of an educational intervention combining training-in-bias and training-in-rules techniques. First, participants directly experienced situations that tend to induce the Barnum effect and the confirmation bias. Thereafter, these effects were explained and examples of their influence over everyday life were provided. Compared to a control group, participants who received the intervention showed diminished causal illusions in a contingency learning task and a decrease in the precognition dimension of a paranormal belief scale. Overall, results suggest that evidence-based educational interventions like the one presented here could be used to significantly improve critical thinking skills in our students. PMID:29385184
Recurrence measure of conditional dependence and applications.

PubMed

Ramos, Antônio M T; Builes-Jaramillo, Alejandro; Poveda, Germán; Goswami, Bedartha; Macau, Elbert E N; Kurths, Jürgen; Marwan, Norbert

2017-05-01

Identifying causal relations from observational data sets has posed great challenges in data-driven causality inference studies. One of the successful approaches to detect direct coupling in the information theory framework is transfer entropy. However, the core of entropy-based tools lies on the probability estimation of the underlying variables. Here we propose a data-driven approach for causality inference that incorporates recurrence plot features into the framework of information theory. We define it as the recurrence measure of conditional dependence (RMCD), and we present some applications. The RMCD quantifies the causal dependence between two processes based on joint recurrence patterns between the past of the possible driver and present of the potentially driven, excepting the contribution of the contemporaneous past of the driven variable. Finally, it can unveil the time scale of the influence of the sea-surface temperature of the Pacific Ocean on the precipitation in the Amazonia during recent major droughts.
Recurrence measure of conditional dependence and applications

NASA Astrophysics Data System (ADS)

Ramos, Antônio M. T.; Builes-Jaramillo, Alejandro; Poveda, Germán; Goswami, Bedartha; Macau, Elbert E. N.; Kurths, Jürgen; Marwan, Norbert

2017-05-01

Identifying causal relations from observational data sets has posed great challenges in data-driven causality inference studies. One of the successful approaches to detect direct coupling in the information theory framework is transfer entropy. However, the core of entropy-based tools lies on the probability estimation of the underlying variables. Here we propose a data-driven approach for causality inference that incorporates recurrence plot features into the framework of information theory. We define it as the recurrence measure of conditional dependence (RMCD), and we present some applications. The RMCD quantifies the causal dependence between two processes based on joint recurrence patterns between the past of the possible driver and present of the potentially driven, excepting the contribution of the contemporaneous past of the driven variable. Finally, it can unveil the time scale of the influence of the sea-surface temperature of the Pacific Ocean on the precipitation in the Amazonia during recent major droughts.
THE RELATION BETWEEN DIFFERENT DIMENSIONS OF ALCOHOL CONSUMPTION AND BURDEN OF DISEASE - AN OVERVIEW

PubMed Central

Rehm, Jürgen; Baliunas, Dolly; Borges, Guilherme L. G.; Graham, Kathryn; lrving, Hyacinth; Kehoe, Tara; Parry, Charles D.; Patra, Jayadeep; Popova, Svetlana; Poznyak, Vladimir; Roerecke, Michael; Room, Robin; Samokhvalov, Andriy V.; Taylor, Benjamin

2012-01-01

AIMS As part of a larger study to estimate the global burden of disease and injury attributable to alcohol: To evaluate the evidence for a causal impact of average volume of alcohol consumption and pattern of drinking on diseases and injuries;To quantify relationships identified as causal based on published meta-analyses;To separate the impact on mortality vs. morbidity where possible; andTo assess the impact of the quality of alcohol on burden of disease. METHODS Systematic literature reviews were used to identify alcohol-related diseases, birth complications and injuries using standard epidemiologic criteria to determine causality. The extent of the risk relations was taken from meta-analyses. RESULTS Evidence of a causal impact of average volume of alcohol consumption was found for the following major diseases: tuberculosis, mouth, nasopharynx, other pharynx and oropharynx cancer, oesophageal cancer, colon and rectum cancer, liver cancer, female breast cancer, diabetes mellitus, alcohol use disorders, unipolar depressive disorders, epilepsy, hypertensive heart disease, ischaemic heart disease (IHD), ischaemic and haemorrhagic stroke, conduction disorders and other dysrhythmias, lower respiratory infections (pneumonia), cirrhosis of the liver, preterm birth complications, foetal alcohol syndrome. Dose-response relationships could be quantified for all disease categories except for depressive disorders, with the relative risk increasing with increased level of alcohol consumption for most diseases. Both average volume and drinking pattern were causally linked to IHD, foetal alcohol syndrome, and unintentional and intentional injuries. For IHD, ischaemic stroke and diabetes mellitus beneficial effects were observed for patterns of light to moderate drinking without heavy drinking occasions (as defined by 60+ grams pure alcohol per day). For several disease and injury categories, the effects were stronger on mortality compared to morbidity. There was insufficient evidence to establish whether quality of alcohol had a major impact on disease burden. CONCLUSIONS Overall, these findings indicate that alcohol causally impacts many disease outcomes, both chronic and acute, and injuries. In addition, a pattern of heavy episodic drinking increases risk for some disease and all injury outcomes. Future studies need to address a number of methodological issues, especially the differential role of average volume versus drinking pattern, in order to obtain more accurate risk estimates and to better understand the nature of alcohol-disease relationships. PMID:20331573
Thinking or feeling? An exploratory study of maternal scaffolding, child mental state talk, and emotion understanding in language-impaired and typically developing school-aged children.

PubMed

Yuill, Nicola; Little, Sarah

2018-06-01

Mother-child mental state talk (MST) supports children's developing social-emotional understanding. In typically developing (TD) children, family conversations about emotion, cognition, and causes have been linked to children's emotion understanding. Specific language impairment (SLI) may compromise developing emotion understanding and adjustment. We investigated emotion understanding in children with SLI and TD, in relation to mother-child conversation. Specifically, is cognitive, emotion, or causal MST more important for child emotion understanding and how might maternal scaffolding support this? Nine 5- to 9-year-old children with SLI and nine age-matched typically developing (TD) children, and their mothers. We assessed children's language, emotion understanding and reported behavioural adjustment. Mother-child conversations were coded for MST, including emotion, cognition, and causal talk, and for scaffolding of causal talk. Children with SLI scored lower than TD children on emotion understanding and adjustment. Mothers in each group provided similar amounts of cognitive, emotion, and causal talk, but SLI children used proportionally less cognitive and causal talk than TD children did, and more such child talk predicted better child emotion understanding. Child emotion talk did not differ between groups and did not predict emotion understanding. Both groups participated in maternal-scaffolded causal talk, but causal talk about emotion was more frequent in TD children, and such talk predicted higher emotion understanding. Cognitive and causal language scaffolded by mothers provides tools for articulating increasingly complex ideas about emotion, predicting children's emotion understanding. Our study provides a robust method for studying scaffolding processes for understanding causes of emotion. © 2017 The British Psychological Society.
Temporal Information of Directed Causal Connectivity in Multi-Trial ERP Data using Partial Granger Causality.

PubMed

Youssofzadeh, Vahab; Prasad, Girijesh; Naeem, Muhammad; Wong-Lin, KongFatt

2016-01-01

Partial Granger causality (PGC) has been applied to analyse causal functional neural connectivity after effectively mitigating confounding influences caused by endogenous latent variables and exogenous environmental inputs. However, it is not known how this connectivity obtained from PGC evolves over time. Furthermore, PGC has yet to be tested on realistic nonlinear neural circuit models and multi-trial event-related potentials (ERPs) data. In this work, we first applied a time-domain PGC technique to evaluate simulated neural circuit models, and demonstrated that the PGC measure is more accurate and robust in detecting connectivity patterns as compared to conditional Granger causality and partial directed coherence, especially when the circuit is intrinsically nonlinear. Moreover, the connectivity in PGC settles faster into a stable and correct configuration over time. After method verification, we applied PGC to reveal the causal connections of ERP trials of a mismatch negativity auditory oddball paradigm. The PGC analysis revealed a significant bilateral but asymmetrical localised activity in the temporal lobe close to the auditory cortex, and causal influences in the frontal, parietal and cingulate cortical areas, consistent with previous studies. Interestingly, the time to reach a stable connectivity configuration (~250–300 ms) coincides with the deviation of ensemble ERPs of oddball from standard tones. Finally, using a sliding time window, we showed higher resolution dynamics of causal connectivity within an ERP trial. In summary, time-domain PGC is promising in deciphering directed functional connectivity in nonlinear and ERP trials accurately, and at a sufficiently early stage. This data-driven approach can reduce computational time, and determine the key architecture for neural circuit modeling.
The Nature of the Association between Moral Neutralization and Aggression: A Systematic Test of Causality in Early Adolescence

ERIC Educational Resources Information Center

Ribeaud, Denis; Eisner, Manuel

2015-01-01

This article examines possible causal linkages between moral neutralization--a generic term for the related concepts of neutralization techniques, moral disengagement, and self-serving cognitive distortions--and aggressive behavior by using a set of repeated measures in a culturally diverse urban sample at ages 11.4 and 13.7 (N = 1,032). First,…
Sensing the Coherence of Biology in Contrast to Psychology: Young Children's Use of Causal Relations to Distinguish Two Foundational Domains

ERIC Educational Resources Information Center

Erickson, Jane E.; Keil, Frank C.; Lockhart, Kristi L.

2010-01-01

To what extent do children understand that biological processes fall into 1 coherent domain unified by distinct causal principles? In Experiments 1 and 2 (N = 125) kindergartners are given triads of biological and psychological processes and asked to identify which 2 members of the triad belong together. Results show that 5-year-olds correctly…
Overview of the Clinical Consult Case Review of Adverse Events Following Immunization: Clinical Immunization Safety Assessment Network (CISA) 2004-2009

PubMed Central

Williams, S Elizabeth; Klein, Nicola P; Halsey, Neal; Dekker, Cornelia L; Baxter, Roger P; Marchant, Colin D; LaRussa, Philip S; Sparks, Robert C; Tokars, Jerome I; Pahud, Barbara A; Aukes, Laurie; Jakob, Kathleen; Coronel, Silvia; Choi, Howard; Slade, Barbara A; Edwards, Kathryn M

2016-01-01

Background In 2004 the Clinical Consult Case Review (CCCR) working group was formed within the CDC-funded Clinical immunization Safety Assessment (CISA) Network to review individual cases of adverse events following immunizations (AEFI). Methods Cases were referred by practitioners, health departments, or CDC employees. Vaccine Adverse Event Reporting System (VAERS) searches and literature reviews for similar cases were performed prior to review. After CCCR discussion, AEFI were assessed for a causal relationship with vaccination and recommendations regarding future immunizations were relayed back to the referring physicians. In 2010, surveys were sent to referring physicians to determine the utility and effectiveness of the CCCR service. Results CISA investigators reviewed 76 cases during 68 conference calls between April 2004 and December 2009. Almost half of cases (35/76) were neurological in nature. Similar AEFI for the specific vaccines received were discovered for 63 cases through VAERS searches and for 38 cases through PubMed searches. Causality assessment using the modified WHO criteria resulted in classifying 3 cases as definitely related to vaccine administration, 12 as probably related, 16 as possibly related, 18 as unlikely related, 10 as unrelated, and 17 had insufficient information to assign causality. The physician satisfaction survey was returned by 30 (57.7%) of those surveyed and a majority of respondents (93.3%) felt that the CCCR service was useful. Conclusions The CCCR provides advice about AEFI to practitioners, assigns potential causality, and contributes to an improved understanding of adverse health events following immunizations. PMID:21801776
Re-Ranking Sequencing Variants in the Post-GWAS Era for Accurate Causal Variant Identification

PubMed Central

Faye, Laura L.; Machiela, Mitchell J.; Kraft, Peter; Bull, Shelley B.; Sun, Lei

2013-01-01

Next generation sequencing has dramatically increased our ability to localize disease-causing variants by providing base-pair level information at costs increasingly feasible for the large sample sizes required to detect complex-trait associations. Yet, identification of causal variants within an established region of association remains a challenge. Counter-intuitively, certain factors that increase power to detect an associated region can decrease power to localize the causal variant. First, combining GWAS with imputation or low coverage sequencing to achieve the large sample sizes required for high power can have the unintended effect of producing differential genotyping error among SNPs. This tends to bias the relative evidence for association toward better genotyped SNPs. Second, re-use of GWAS data for fine-mapping exploits previous findings to ensure genome-wide significance in GWAS-associated regions. However, using GWAS findings to inform fine-mapping analysis can bias evidence away from the causal SNP toward the tag SNP and SNPs in high LD with the tag. Together these factors can reduce power to localize the causal SNP by more than half. Other strategies commonly employed to increase power to detect association, namely increasing sample size and using higher density genotyping arrays, can, in certain common scenarios, actually exacerbate these effects and further decrease power to localize causal variants. We develop a re-ranking procedure that accounts for these adverse effects and substantially improves the accuracy of causal SNP identification, often doubling the probability that the causal SNP is top-ranked. Application to the NCI BPC3 aggressive prostate cancer GWAS with imputation meta-analysis identified a new top SNP at 2 of 3 associated loci and several additional possible causal SNPs at these loci that may have otherwise been overlooked. This method is simple to implement using R scripts provided on the author's website. PMID:23950724
Assessing the causal role of adiposity on disordered eating in childhood, adolescence, and adulthood: a Mendelian randomization analysis

PubMed Central

2017-01-01

Background: Observational studies have shown that higher body mass index (BMI) is associated with increased risk of developing disordered eating patterns. However, the causal direction of this relation remains ambiguous. Objective: We used Mendelian randomization (MR) to infer the direction of causality between BMI and disordered eating in childhood, adolescence, and adulthood. Design: MR analyses were conducted with a genetic score as an instrumental variable for BMI to assess the causal effect of BMI at age 7 y on disordered eating patterns at age 13 y with the use of data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 4473). To examine causality in the reverse direction, MR analyses were used to estimate the effect of the same disordered eating patterns at age 13 y on BMI at age 17 y via a split-sample approach in the ALSPAC. We also investigated the causal direction of the association between BMI and eating disorders (EDs) in adults via a two-sample MR approach and publically available genome-wide association study data. Results: MR results indicated that higher BMI at age 7 y likely causes higher levels of binge eating and overeating, weight and shape concerns, and weight-control behavior patterns in both males and females and food restriction in males at age 13 y. Furthermore, results suggested that higher levels of binge eating and overeating in males at age 13 y likely cause higher BMI at age 17 y. We showed no evidence of causality between BMI and EDs in adulthood in either direction. Conclusions: This study provides evidence to suggest a causal effect of higher BMI in childhood and increased risk of disordered eating at age 13 y. Furthermore, higher levels of binge eating and overeating may cause higher BMI in later life. These results encourage an exploration of the ways to break the causal chain between these complex phenotypes, which could inform and prevent disordered eating problems in adolescence. PMID:28747331
Assessing the causal role of adiposity on disordered eating in childhood, adolescence, and adulthood: a Mendelian randomization analysis.

PubMed

Reed, Zoe E; Micali, Nadia; Bulik, Cynthia M; Davey Smith, George; Wade, Kaitlin H

2017-09-01

Background: Observational studies have shown that higher body mass index (BMI) is associated with increased risk of developing disordered eating patterns. However, the causal direction of this relation remains ambiguous. Objective: We used Mendelian randomization (MR) to infer the direction of causality between BMI and disordered eating in childhood, adolescence, and adulthood. Design: MR analyses were conducted with a genetic score as an instrumental variable for BMI to assess the causal effect of BMI at age 7 y on disordered eating patterns at age 13 y with the use of data from the Avon Longitudinal Study of Parents and Children (ALSPAC) ( n = 4473). To examine causality in the reverse direction, MR analyses were used to estimate the effect of the same disordered eating patterns at age 13 y on BMI at age 17 y via a split-sample approach in the ALSPAC. We also investigated the causal direction of the association between BMI and eating disorders (EDs) in adults via a two-sample MR approach and publically available genome-wide association study data. Results: MR results indicated that higher BMI at age 7 y likely causes higher levels of binge eating and overeating, weight and shape concerns, and weight-control behavior patterns in both males and females and food restriction in males at age 13 y. Furthermore, results suggested that higher levels of binge eating and overeating in males at age 13 y likely cause higher BMI at age 17 y. We showed no evidence of causality between BMI and EDs in adulthood in either direction. Conclusions: This study provides evidence to suggest a causal effect of higher BMI in childhood and increased risk of disordered eating at age 13 y. Furthermore, higher levels of binge eating and overeating may cause higher BMI in later life. These results encourage an exploration of the ways to break the causal chain between these complex phenotypes, which could inform and prevent disordered eating problems in adolescence.
Detecting causal drivers and empirical prediction of the Indian Summer Monsoon

NASA Astrophysics Data System (ADS)

Di Capua, G.; Vellore, R.; Raghavan, K.; Coumou, D.

2017-12-01

The Indian summer monsoon (ISM) is crucial for the economy, society and natural ecosystems on the Indian peninsula. Predict the total seasonal rainfall at several months lead time would help to plan effective water management strategies, improve flood or drought protection programs and prevent humanitarian crisis. However, the complexity and strong internal variability of the ISM circulation system make skillful seasonal forecasting challenging. Moreover, to adequately identify the low-frequency, and far-away processes which influence ISM behavior novel tools are needed. We applied a Response-Guided Causal Precursor Detection (RGCPD) scheme, which is a novel empirical prediction method which unites a response-guided community detection scheme with a causal discovery algorithm (CEN). These tool allow us to assess causal pathways between different components of the ISM circulation system and with far-away regions in the tropics, mid-latitudes or Arctic. The scheme has successfully been used to identify causal precursors of the Stratospheric polar vortex enabling skillful predictions at (sub) seasonal timescales (Kretschmer et al. 2016, J.Clim., Kretschmer et al. 2017, GRL). We analyze observed ISM monthly rainfall over the monsoon trough region. Applying causal discovery techniques, we identify several causal precursor communities in the fields of 2m-temperature, sea level pressure and snow depth over Eurasia. Specifically, our results suggest that surface temperature conditions in both tropical and Arctic regions contribute to ISM variability. A linear regression prediction model based on the identified set of communities has good hindcasting skills with 4-5 months lead times. Further we separate El Nino, La Nina and ENSO-neutral years from each other and find that the causal precursors are different dependent on ENSO state. The ENSO-state dependent causal precursors give even higher skill, especially for La Nina years when the ISM is relatively strong. These findings are promising results that might ultimately contribute to both improved understanding of the ISM circulation system and help improving seasonal ISM forecasts.
Genetic regulation of gene expression in the lung identifies CST3 and CD22 as potential causal genes for airflow obstruction.

PubMed

Lamontagne, Maxime; Timens, Wim; Hao, Ke; Bossé, Yohan; Laviolette, Michel; Steiling, Katrina; Campbell, Joshua D; Couture, Christian; Conti, Massimo; Sherwood, Karen; Hogg, James C; Brandsma, Corry-Anke; van den Berge, Maarten; Sandford, Andrew; Lam, Stephen; Lenburg, Marc E; Spira, Avrum; Paré, Peter D; Nickle, David; Sin, Don D; Postma, Dirkje S

2014-11-01

COPD is a complex chronic disease with poorly understood pathogenesis. Integrative genomic approaches have the potential to elucidate the biological networks underlying COPD and lung function. We recently combined genome-wide genotyping and gene expression in 1111 human lung specimens to map expression quantitative trait loci (eQTL). To determine causal associations between COPD and lung function-associated single nucleotide polymorphisms (SNPs) and lung tissue gene expression changes in our lung eQTL dataset. We evaluated causality between SNPs and gene expression for three COPD phenotypes: FEV(1)% predicted, FEV(1)/FVC and COPD as a categorical variable. Different models were assessed in the three cohorts independently and in a meta-analysis. SNPs associated with a COPD phenotype and gene expression were subjected to causal pathway modelling and manual curation. In silico analyses evaluated functional enrichment of biological pathways among newly identified causal genes. Biologically relevant causal genes were validated in two separate gene expression datasets of lung tissues and bronchial airway brushings. High reliability causal relations were found in SNP-mRNA-phenotype triplets for FEV(1)% predicted (n=169) and FEV(1)/FVC (n=80). Several genes of potential biological relevance for COPD were revealed. eQTL-SNPs upregulating cystatin C (CST3) and CD22 were associated with worse lung function. Signalling pathways enriched with causal genes included xenobiotic metabolism, apoptosis, protease-antiprotease and oxidant-antioxidant balance. By using integrative genomics and analysing the relationships of COPD phenotypes with SNPs and gene expression in lung tissue, we identified CST3 and CD22 as potential causal genes for airflow obstruction. This study also augmented the understanding of previously described COPD pathways. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
Genetic evidence for causal relationships between maternal obesity-related traits and birth weight

PubMed Central

Tyrrell, Jessica; Richmond, Rebecca C.; Palmer, Tom M.; Feenstra, Bjarke; Rangarajan, Janani; Metrustry, Sarah; Cavadino, Alana; Paternoster, Lavinia; Armstrong, Loren L.; De Silva, N. Maneka G.; Wood, Andrew R.; Horikoshi, Momoko; Geller, Frank; Myhre, Ronny; Bradfield, Jonathan P.; Kreiner-Møller, Eskil; Huikari, Ville; Painter, Jodie N.; Hottenga, Jouke-Jan; Allard, Catherine; Berry, Diane J.; Bouchard, Luigi; Das, Shikta; Evans, David M.; Hakonarson, Hakon; Hayes, M. Geoffrey; Heikkinen, Jani; Hofman, Albert; Knight, Bridget; Lind, Penelope A.; McCarthy, Mark I.; McMahon, George; Medland, Sarah E.; Melbye, Mads; Morris, Andrew P.; Nodzenski, Michael; Reichetzeder, Christoph; Ring, Susan M.; Sebert, Sylvain; Sengpiel, Verena; Sørensen, Thorkild I.A.; Willemsen, Gonneke; de Geus, Eco J. C.; Martin, Nicholas G.; Spector, Tim D.; Power, Christine; Järvelin, Marjo-Riitta; Bisgaard, Hans; Grant, Struan F.A.; Nohr, Ellen A.; Jaddoe, Vincent W.; Jacobsson, Bo; Murray, Jeffrey C.; Hocher, Berthold; Hattersley, Andrew T.; Scholtens, Denise M.; Smith, George Davey; Hivert, Marie-France; Felix, Janine F.; Hyppönen, Elina; Lowe, William L.; Frayling, Timothy M.; Lawlor, Debbie A.; Freathy, Rachel M.

2016-01-01

Structured abstract Importance Neonates born to overweight/obese women are larger and at higher risk of birth complications. Many maternal obesity-related traits are observationally associated with birth weight, but the causal nature of these associations is uncertain. Objective To test for genetic evidence of causal associations of maternal body mass index (BMI) and related traits with birth weight. Design, Setting and Participants We used Mendelian randomization to test whether maternal BMI and obesity-related traits are causally related to offspring birth weight. Mendelian randomization makes use of the fact that genotypes are randomly determined at conception and are thus not confounded by non-genetic factors. Data were analysed on 30,487 women from 18 studies. Participants were of European ancestry from population- or community-based studies located in Europe, North America or Australia and participating in the Early Growth Genetics (EGG) Consortium. Live, term, singleton offspring born between 1929 and 2013 were included. We tested associations between a genetic score of 30 BMI-associated single nucleotide polymorphisms (SNPs) and (i) maternal BMI and (ii) birth weight, to estimate the causal relationship between BMI and birth weight. Analyses were repeated for other obesity-related traits. Exposures Genetic scores for BMI, fasting glucose level, type 2 diabetes, systolic blood pressure (SBP), triglyceride level, HDL-cholesterol level, vitamin D status and adiponectin level. Main Outcome(s) and Measure(s) Offspring birth weight measured by trained study personnel (n=2 studies), from medical records (n= 10 studies) or from maternal report (n=6 studies). Results Among the 30,487 newborns the mean birth weight in the various cohorts ranged from 3325 g to 3679 g. The genetic score for BMI was associated with a 2g (95%CI: 0, 3g) higher offspring birth weight per maternal BMI-raising allele (P=0.008). The maternal genetic scores for fasting glucose and SBP were also associated with birth weight with effect sizes of 8g (95%CI: 6, 10g) per glucose-raising allele (P=7×10−14) and −4g (95%CI: −6, −2g) per SBP-raising allele (P=1×10−5), respectively. A 1 standard deviation (1 SD ≈ 4kg/m2) genetically higher maternal BMI was associated with a 55g (95% CI: 17, 93g) higher birth weight. A 1-SD genetically higher maternal fasting glucose (≈ 0.4mmol/L) or SBP (10mmHg) were associated with a 114g (95%CI: 80, 147g) higher or −208g (95% CI: −394, −21g) lower birth weight, respectively. For BMI and fasting glucose these genetic associations were consistent with the observational associations, but for SBP, the genetic and observational associations were in opposite directions. Conclusions and Relevance In this Mendelian randomization study of more than 30,000 women with singleton offspring from 18 studies, genetically elevated maternal BMI and blood glucose levels were potentially causally associated with higher offspring birth weight, whereas genetically elevated maternal systolic blood pressure was shown to be potentially causally related to lower birth weight. If replicated, these findings may have implications for counseling and managing pregnancies to avoid adverse weight-related birth outcomes. PMID:26978208
The Role of Adiposity in Cardiometabolic Traits: A Mendelian Randomization Analysis

PubMed Central

Ploner, Alexander; Fischer, Krista; Horikoshi, Momoko; Sarin, Antti-Pekka; Thorleifsson, Gudmar; Ladenvall, Claes; Kals, Mart; Kuningas, Maris; Draisma, Harmen H. M.; Ried, Janina S.; van Zuydam, Natalie R.; Huikari, Ville; Mangino, Massimo; Sonestedt, Emily; Benyamin, Beben; Nelson, Christopher P.; Rivera, Natalia V.; Kristiansson, Kati; Shen, Huei-yi; Havulinna, Aki S.; Dehghan, Abbas; Donnelly, Louise A.; Kaakinen, Marika; Nuotio, Marja-Liisa; Robertson, Neil; de Bruijn, Renée F. A. G.; Ikram, M. Arfan; Amin, Najaf; Balmforth, Anthony J.; Braund, Peter S.; Doney, Alexander S. F.; Döring, Angela; Elliott, Paul; Esko, Tõnu; Franco, Oscar H.; Gretarsdottir, Solveig; Hartikainen, Anna-Liisa; Heikkilä, Kauko; Herzig, Karl-Heinz; Holm, Hilma; Hottenga, Jouke Jan; Hyppönen, Elina; Illig, Thomas; Isaacs, Aaron; Isomaa, Bo; Karssen, Lennart C.; Kettunen, Johannes; Koenig, Wolfgang; Kuulasmaa, Kari; Laatikainen, Tiina; Laitinen, Jaana; Lindgren, Cecilia; Lyssenko, Valeriya; Läärä, Esa; Rayner, Nigel W.; Männistö, Satu; Pouta, Anneli; Rathmann, Wolfgang; Rivadeneira, Fernando; Ruokonen, Aimo; Savolainen, Markku J.; Sijbrands, Eric J. G.; Small, Kerrin S.; Smit, Jan H.; Steinthorsdottir, Valgerdur; Syvänen, Ann-Christine; Taanila, Anja; Tobin, Martin D.; Uitterlinden, Andre G.; Willems, Sara M.; Willemsen, Gonneke; Witteman, Jacqueline; Perola, Markus; Evans, Alun; Ferrières, Jean; Virtamo, Jarmo; Kee, Frank; Tregouet, David-Alexandre; Arveiler, Dominique; Amouyel, Philippe; Ferrario, Marco M.; Brambilla, Paolo; Hall, Alistair S.; Heath, Andrew C.; Madden, Pamela A. F.; Martin, Nicholas G.; Montgomery, Grant W.; Whitfield, John B.; Jula, Antti; Knekt, Paul; Oostra, Ben; van Duijn, Cornelia M.; Penninx, Brenda W. J. H.; Davey Smith, George; Kaprio, Jaakko; Samani, Nilesh J.; Gieger, Christian; Peters, Annette; Wichmann, H.-Erich; Boomsma, Dorret I.; de Geus, Eco J. C.; Tuomi, TiinaMaija; Power, Chris; Hammond, Christopher J.; Spector, Tim D.; Lind, Lars; Orho-Melander, Marju; Palmer, Colin Neil Alexander; Morris, Andrew D.; Groop, Leif; Järvelin, Marjo-Riitta; Salomaa, Veikko; Vartiainen, Erkki; Hofman, Albert; Ripatti, Samuli; Metspalu, Andres; Thorsteinsdottir, Unnur; Stefansson, Kari; Pedersen, Nancy L.; McCarthy, Mark I.; Ingelsson, Erik; Prokopenko, Inga

2013-01-01

Background The association between adiposity and cardiometabolic traits is well known from epidemiological studies. Whilst the causal relationship is clear for some of these traits, for others it is not. We aimed to determine whether adiposity is causally related to various cardiometabolic traits using the Mendelian randomization approach. Methods and Findings We used the adiposity-associated variant rs9939609 at the FTO locus as an instrumental variable (IV) for body mass index (BMI) in a Mendelian randomization design. Thirty-six population-based studies of individuals of European descent contributed to the analyses. Age- and sex-adjusted regression models were fitted to test for association between (i) rs9939609 and BMI (n = 198,502), (ii) rs9939609 and 24 traits, and (iii) BMI and 24 traits. The causal effect of BMI on the outcome measures was quantified by IV estimators. The estimators were compared to the BMI–trait associations derived from the same individuals. In the IV analysis, we demonstrated novel evidence for a causal relationship between adiposity and incident heart failure (hazard ratio, 1.19 per BMI-unit increase; 95% CI, 1.03–1.39) and replicated earlier reports of a causal association with type 2 diabetes, metabolic syndrome, dyslipidemia, and hypertension (odds ratio for IV estimator, 1.1–1.4; all p<0.05). For quantitative traits, our results provide novel evidence for a causal effect of adiposity on the liver enzymes alanine aminotransferase and gamma-glutamyl transferase and confirm previous reports of a causal effect of adiposity on systolic and diastolic blood pressure, fasting insulin, 2-h post-load glucose from the oral glucose tolerance test, C-reactive protein, triglycerides, and high-density lipoprotein cholesterol levels (all p<0.05). The estimated causal effects were in agreement with traditional observational measures in all instances except for type 2 diabetes, where the causal estimate was larger than the observational estimate (p = 0.001). Conclusions We provide novel evidence for a causal relationship between adiposity and heart failure as well as between adiposity and increased liver enzymes. Please see later in the article for the Editors' Summary PMID:23824655
Causal Entropies – a measure for determining changes in the temporal organization of neural systems

PubMed Central

Waddell, Jack; Dzakpasu, Rhonda; Booth, Victoria; Riley, Brett; Reasor, Jonathan; Poe, Gina; Zochowski, Michal

2009-01-01

We propose a novel measure to detect temporal ordering in the activity of individual neurons in a local network, which is thought to be a hallmark of activity-dependent synaptic modifications during learning. The measure, called Causal Entropy, is based on the time-adaptive detection of asymmetries in the relative temporal patterning between neuronal pairs. We characterize properties of the measure on both simulated data and experimental multiunit recordings of hippocampal neurons from the awake, behaving rat, and show that the metric can more readily detect those asymmetries than standard cross correlation-based techniques, especially since the temporal sensitivity of causal entropy can detect such changes rapidly and dynamically. PMID:17275095

The causal relation between turbulent particle flux and density gradient

DOE Office of Scientific and Technical Information (OSTI.GOV)

Milligen, B. Ph. van; Martín de Aguilera, A.; Hidalgo, C.

A technique for detecting the causal relationship between fluctuating signals is used to investigate the relation between flux and gradient in fusion plasmas. Both a resistive pressure gradient driven turbulence model and experimental Langmuir probe data from the TJ-II stellarator are studied. It is found that the maximum influence occurs at a finite time lag (non-instantaneous response) and that quasi-periodicities exist. Furthermore, the model results show very long range radial influences, extending over most of the investigated regions, possibly related to coupling effects associated with plasma self-organization. These results clearly show that transport in fusion plasmas is not local andmore » instantaneous, as is sometimes assumed.« less
Environmental influences on energy balance-related behaviors: A dual-process view

PubMed Central

Kremers, Stef PJ; de Bruijn, Gert-Jan; Visscher, Tommy LS; van Mechelen, Willem; de Vries, Nanne K; Brug, Johannes

2006-01-01

Background Studies on the impact of the 'obesogenic' environment have often used non-theoretical approaches. In this journal's debate and in other papers authors have argued the necessity of formulating conceptual models for differentiating the causal role of environmental influences on behavior. Discussion The present paper aims to contribute to the debate by presenting a dual-process view on the environment – behavior relationship. This view is conceptualized in the EnRG framework (Environmental Research framework for weight Gain prevention). In the framework, behavior is postulated to be the result of a simultaneous influence of conscious and unconscious processes. Environmental influences are hypothesized to influence behavior both indirectly and directly. The indirect causal mechanism reflects the mediating role of behavior-specific cognitions in the influence of the environment on behavior. A direct influence reflects the automatic, unconscious, influence of the environment on behavior. Specific personal and behavioral factors are postulated to moderate the causal path (i.e., inducing either the automatic or the cognitively mediated environment – behavior relation). In addition, the EnRG framework applies an energy balance-approach, stimulating the integrated study of determinants of diet and physical activity. Conclusion The application of a dual-process view may guide research towards causal mechanisms linking specific environmental features with energy balance-related behaviors in distinct populations. The present paper is hoped to contribute to the evolution of a paradigm that may help to disentangle the role of 'obesogenic' environmental factors. PMID:16700907
Causal Effect of Self-esteem on Cigarette Smoking Stages in Adolescents: Coarsened Exact Matching in a Longitudinal Study.

PubMed

Khosravi, Ahmad; Mohammadpoorasl, Asghar; Holakouie-Naieni, Kourosh; Mahmoodi, Mahmood; Pouyan, Ali Akbar; Mansournia, Mohammad Ali

2016-12-01

Identification of the causal impact of self-esteem on smoking stages faces seemingly insurmountable problems in observational data, where self-esteem is not manipulable by the researcher and cannot be assigned randomly. The aim of this study was to find out if weaker self-esteem in adolescence is a risk factor of cigarette smoking in a longitudinal study in Iran. In this longitudinal study, 4,853 students (14-18 years) completed a self-administered multiple-choice anonym questionnaire. The students were evaluated twice, 12 months apart. Students were matched based on coarsened exact matching on pretreatment variables, including age, gender, smoking stages at the first wave of study, socioeconomic status, general risk-taking behavior, having a smoker in the family, having a smoker friend, attitude toward smoking, and self-injury, to ensure statistically equivalent comparison groups. Self-esteem was measured using the Rosenberg 10-item questionnaire and were classified using a latent class analysis. After matching, the effect of self-esteem was evaluated using a multinomial logistic model. In the causal fitted model, for adolescents with weaker self-esteem relative to those with stronger self-esteem, the relative risk for experimenters and regular smokers relative to nonsmokers would be expected to increase by a factor of 2.2 (1.9-2.6) and 2.0 (1.5-2.6), respectively. Using a causal approach, our study indicates that low self-esteem is consistently associated with progression in cigarette smoking stages.
Causal relationships among academic delay of gratification, motivation, and self-regulated learning in elementary school children.

PubMed

Zhang, Lili; Maruno, Shun'ichi

2010-10-01

Academic delay of gratification refers to the postponement of immediate rewards by students and the pursuit of more important, temporally remote academic goals. A path model was designed to identify the causal relationships among academic delay of gratification and motivation, self-regulated learning strategies (as specified in the Motivated Strategies for Learning Questionnaire), and grades among 386 Chinese elementary school children. Academic delay of gratification was found to be positively related to motivation and metacognition. Cognitive strategy, resource management, and grades mediated these two factors and were indirectly related to academic delay of gratification.
Darwin revisited: The vagus nerve is a causal element in controlling recognition of other's emotions.

PubMed

Colzato, Lorenza S; Sellaro, Roberta; Beste, Christian

2017-07-01

Charles Darwin proposed that via the vagus nerve, the tenth cranial nerve, emotional facial expressions are evolved, adaptive and serve a crucial communicative function. In line with this idea, the later-developed polyvagal theory assumes that the vagus nerve is the key phylogenetic substrate that regulates emotional and social behavior. The polyvagal theory assumes that optimal social interaction, which includes the recognition of emotion in faces, is modulated by the vagus nerve. So far, in humans, it has not yet been demonstrated that the vagus plays a causal role in emotion recognition. To investigate this we employed transcutaneous vagus nerve stimulation (tVNS), a novel non-invasive brain stimulation technique that modulates brain activity via bottom-up mechanisms. A sham/placebo-controlled, randomized cross-over within-subjects design was used to infer a causal relation between the stimulated vagus nerve and the related ability to recognize emotions as indexed by the Reading the Mind in the Eyes Test in 38 healthy young volunteers. Active tVNS, compared to sham stimulation, enhanced emotion recognition for easy items, suggesting that it promoted the ability to decode salient social cues. Our results confirm that the vagus nerve is causally involved in emotion recognition, supporting Darwin's argumentation. Copyright © 2017 Elsevier Ltd. All rights reserved.
Evaluating Alcoholics Anonymous's effect on drinking in Project MATCH using cross-lagged regression panel analysis.

PubMed

Magura, Stephen; Cleland, Charles M; Tonigan, J Scott

2013-05-01

The objective of the study is to determine whether Alcoholics Anonymous (AA) participation leads to reduced drinking and problems related to drinking within Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity), an existing national alcoholism treatment data set. The method used is structural equation modeling of panel data with cross-lagged partial regression coefficients. The main advantage of this technique for the analysis of AA outcomes is that potential reciprocal causation between AA participation and drinking behavior can be explicitly modeled through the specification of finite causal lags. For the outpatient subsample (n = 952), the results strongly support the hypothesis that AA attendance leads to increases in alcohol abstinence and reduces drinking/ problems, whereas a causal effect in the reverse direction is unsupported. For the aftercare subsample (n = 774), the results are not as clear but also suggest that AA attendance leads to better outcomes. Although randomized controlled trials are the surest means of establishing causal relations between interventions and outcomes, such trials are rare in AA research for practical reasons. The current study successfully exploited the multiple data waves in Project MATCH to examine evidence of causality between AA participation and drinking outcomes. The study obtained unique statistical results supporting the effectiveness of AA primarily in the context of primary outpatient treatment for alcoholism.
Causal explanations of distress and general practitioners' assessments of common mental disorder among punjabi and English attendees.

PubMed

Bhui, Kamaldeep; Bhugra, Dinesh; Goldberg, David

2002-01-01

The literature on the primary care assessment of mental distress among Indian subcontinent origin patients suggests frequent presentations to general practitioner, but rarely for recognisable psychiatric disorders. This study investigates whether cultural variations in patients' causal explanatory models account for cultural variations in the assessment of non-psychotic mental disorders in primary care. In a two-phase survey, 272 Punjabi and 269 English subjects were screened. The second phase was completed by 209 and 180 subjects, respectively. Causal explanatory models were elicited as explanations of two vignette scenarios. One of these emphasised a somatic presentation and the other anxiety symptoms. Psychiatric disorder was assessed by GPs on a Likert scale and by a psychiatrist on the Clinical Interview Schedule. Punjabis more commonly expressed medical/somatic and religious beliefs. General practitioners were more likely to assess any subject giving psychological explanations to vignette A and English subjects giving religious explanations to vignette B as having a significant psychiatric disorder. Where medical/somatic explanations of distress were most prevalent in response to the somatic vignette, psychological, religious and work explanations were less prevalent among Punjabis but not among English subjects. Causal explanations did not fully explain cultural differences in assessments. General practitioners' assessments and causal explanations are related and influenced by culture, but causal explanations do not fully explain cultural differences in assessments.
Disentangling the causal relationships between work-home interference and employee health.

PubMed

van Hooff, Madelon L M; Geurts, Sabine A E; Taris, Toon W; Kompier, Michiel A J; Dikkers, Josje S E; Houtman, Irene L D; van den Heuvel, Floor M M

2005-02-01

The present study was designed to investigate the causal relationships between (time- and strain-based) work-home interference and employee health. The effort-recovery theory provided the theoretical basis for this study. Two-phase longitudinal data (with a 1-year time lag) were gathered from 730 Dutch police officers to test the following hypotheses with structural equation modeling: (i) work-home interference predicts health deterioration, (ii) health complaints precede increased levels of such interference, and (iii) both processes operate. The relationship between stable and changed levels of work-home interference across time and their relationships with the course of health were tested with a group-by-time analysis of variance. Four subgroups were created that differed in starting point and the development of work-home interference across time. The normal causal model, in which strain-based (but not time-based) work-home interference was longitudinally related to increased health complaints 1 year later, fit the data well and significantly better than the reversed causal model. Although the reciprocal model also provided a good fit, it was less parsimonious than the normal causal model. In addition, both an increment in (strain-based) work-home interference across time and a long-lasting experience of high (strain-based) work-home interference were associated with a deterioration in health. It was concluded that (strain-based) work-home interference acts as a precursor of health impairment and that different patterns of (strain-based) work-home interference across time are related to different health courses. Particularly long-term experience of (strain-based) work-home interference seems responsible for an accumulation of health complaints.
Individuals Who Believe in the Paranormal Expose Themselves to Biased Information and Develop More Causal Illusions than Nonbelievers in the Laboratory.

PubMed

Blanco, Fernando; Barberia, Itxaso; Matute, Helena

2015-01-01

In the reasoning literature, paranormal beliefs have been proposed to be linked to two related phenomena: a biased perception of causality and a biased information-sampling strategy (believers tend to test fewer hypotheses and prefer confirmatory information). In parallel, recent contingency learning studies showed that, when two unrelated events coincide frequently, individuals interpret this ambiguous pattern as evidence of a causal relationship. Moreover, the latter studies indicate that sampling more cause-present cases than cause-absent cases strengthens the illusion. If paranormal believers actually exhibit a biased exposure to the available information, they should also show this bias in the contingency learning task: they would in fact expose themselves to more cause-present cases than cause-absent trials. Thus, by combining the two traditions, we predicted that believers in the paranormal would be more vulnerable to developing causal illusions in the laboratory than nonbelievers because there is a bias in the information they experience. In this study, we found that paranormal beliefs (measured using a questionnaire) correlated with causal illusions (assessed by using contingency judgments). As expected, this correlation was mediated entirely by the believers' tendency to expose themselves to more cause-present cases. The association between paranormal beliefs, biased exposure to information, and causal illusions was only observed for ambiguous materials (i.e., the noncontingent condition). In contrast, the participants' ability to detect causal relationships which did exist (i.e., the contingent condition) was unaffected by their susceptibility to believe in paranormal phenomena.
A Hierarchical Causal Taxonomy of Psychopathology across the Life Span

PubMed Central

Lahey, Benjamin B.; Krueger, Robert F.; Rathouz, Paul J.; Waldman, Irwin D.; Zald, David H.

2016-01-01

We propose a taxonomy of psychopathology based on patterns of shared causal influences identified in a review of multivariate behavior genetic studies that distinguish genetic and environmental influences that are either common to multiple dimensions of psychopathology or unique to each dimension. At the phenotypic level, first-order dimensions are defined by correlations among symptoms; correlations among first-order dimensions similarly define higher-order domains (e.g., internalizing or externalizing psychopathology). We hypothesize that the robust phenotypic correlations among first-order dimensions reflect a hierarchy of increasingly specific etiologic influences. Some nonspecific etiologic factors increase risk for all first-order dimensions of psychopathology to varying degrees through a general factor of psychopathology. Other nonspecific etiologic factors increase risk only for all first-order dimensions within a more specific higher-order domain. Furthermore, each first-order dimension has its own unique causal influences. Genetic and environmental influences common to family members tend to be nonspecific, whereas environmental influences unique to each individual are more dimension-specific. We posit that these causal influences on psychopathology are moderated by sex and developmental processes. This causal taxonomy also provides a novel framework for understanding the heterogeneity of each first-order dimension: Different persons exhibiting similar symptoms may be influenced by different combinations of etiologic influences from each of the three levels of the etiologic hierarchy. Furthermore, we relate the proposed causal taxonomy to transdimensional psychobiological processes, which also impact the heterogeneity of each psychopathology dimension. This causal taxonomy implies the need for changes in strategies for studying the etiology, psychobiology, prevention, and treatment of psychopathology. PMID:28004947
Individuals Who Believe in the Paranormal Expose Themselves to Biased Information and Develop More Causal Illusions than Nonbelievers in the Laboratory

PubMed Central

Blanco, Fernando; Barberia, Itxaso; Matute, Helena

2015-01-01

In the reasoning literature, paranormal beliefs have been proposed to be linked to two related phenomena: a biased perception of causality and a biased information-sampling strategy (believers tend to test fewer hypotheses and prefer confirmatory information). In parallel, recent contingency learning studies showed that, when two unrelated events coincide frequently, individuals interpret this ambiguous pattern as evidence of a causal relationship. Moreover, the latter studies indicate that sampling more cause-present cases than cause-absent cases strengthens the illusion. If paranormal believers actually exhibit a biased exposure to the available information, they should also show this bias in the contingency learning task: they would in fact expose themselves to more cause-present cases than cause-absent trials. Thus, by combining the two traditions, we predicted that believers in the paranormal would be more vulnerable to developing causal illusions in the laboratory than nonbelievers because there is a bias in the information they experience. In this study, we found that paranormal beliefs (measured using a questionnaire) correlated with causal illusions (assessed by using contingency judgments). As expected, this correlation was mediated entirely by the believers' tendency to expose themselves to more cause-present cases. The association between paranormal beliefs, biased exposure to information, and causal illusions was only observed for ambiguous materials (i.e., the noncontingent condition). In contrast, the participants' ability to detect causal relationships which did exist (i.e., the contingent condition) was unaffected by their susceptibility to believe in paranormal phenomena. PMID:26177025
A Quantum Probability Model of Causal Reasoning

PubMed Central

Trueblood, Jennifer S.; Busemeyer, Jerome R.

2012-01-01

People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal inference problems produce unexpected results and describes a quantum inference model based on the axiomatic principles of quantum probability theory that can explain these effects. Two of the three phenomena arise from the comparison of predictive judgments (i.e., the conditional probability of an effect given a cause) with diagnostic judgments (i.e., the conditional probability of a cause given an effect). The third phenomenon is a new finding examining order effects in predictive causal judgments. The quantum inference model uses the notion of incompatibility among different causes to account for all three phenomena. Psychologically, the model assumes that individuals adopt different points of view when thinking about different causes. The model provides good fits to the data and offers a coherent account for all three causal reasoning effects thus proving to be a viable new candidate for modeling human judgment. PMID:22593747
Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory

PubMed Central

Gopnik, Alison; Wellman, Henry M.

2012-01-01

We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists. PMID:22582739
[Social determinants of odontalgia in epidemiological studies: theoretical review and proposed conceptual model].

PubMed

Bastos, João Luiz Dornelles; Gigante, Denise Petrucci; Peres, Karen Glazer; Nedel, Fúlvio Borges

2007-01-01

The epidemiological literature has been limited by the absence of a theoretical framework reflecting the complexity of causal mechanisms for the occurrence of health phenomena / disease conditions. In the field of oral epidemiology, such lack of theory also prevails, since dental caries the leading topic in oral research has been often studied through a biological and reductionist viewpoint. One of the most important consequences of dental caries is dental pain (odontalgia), which has received little attention in studies with sophisticated theoretical models and powerful designs to establish causal relationships. The purpose of this study is to review the scientific literature on the determinants of odontalgia and to discuss theories proposed for the explanation of the phenomenon. Conceptual models and emerging theories on the social determinants of oral health are revised, in an attempt to build up links with the bio-psychosocial pain model, proposing a more elaborate causal model for odontalgia. The framework suggests causal pathways between social structure and oral health through material, psychosocial and behavioral pathways. Aspects of the social structure are highlighted in order to relate them to odontalgia, stressing their importance in discussions of causal relationships in oral health research.
Exposure to Traffic-related Air Pollution During Pregnancy and Term Low Birth Weight: Estimation of Causal Associations in a Semiparametric Model

PubMed Central

Padula, Amy M.; Mortimer, Kathleen; Hubbard, Alan; Lurmann, Frederick; Jerrett, Michael; Tager, Ira B.

2012-01-01

Traffic-related air pollution is recognized as an important contributor to health problems. Epidemiologic analyses suggest that prenatal exposure to traffic-related air pollutants may be associated with adverse birth outcomes; however, there is insufficient evidence to conclude that the relation is causal. The Study of Air Pollution, Genetics and Early Life Events comprises all births to women living in 4 counties in California's San Joaquin Valley during the years 2000–2006. The probability of low birth weight among full-term infants in the population was estimated using machine learning and targeted maximum likelihood estimation for each quartile of traffic exposure during pregnancy. If everyone lived near high-volume freeways (approximated as the fourth quartile of traffic density), the estimated probability of term low birth weight would be 2.27% (95% confidence interval: 2.16, 2.38) as compared with 2.02% (95% confidence interval: 1.90, 2.12) if everyone lived near smaller local roads (first quartile of traffic density). Assessment of potentially causal associations, in the absence of arbitrary model assumptions applied to the data, should result in relatively unbiased estimates. The current results support findings from previous studies that prenatal exposure to traffic-related air pollution may adversely affect birth weight among full-term infants. PMID:23045474
Sexual Harassment, Psychological Distress, and Problematic Drinking Behavior Among College Students: An Examination of Reciprocal Causal Relations.

PubMed

Wolff, Jennifer M; Rospenda, Kathleen M; Colaneri, Anthony S

2017-01-01

Sexual harassment on college campuses is a frequent occurrence and serious public health concern. Victims of sexual harassment are at risk for many possible negative health consequences. In addition, certain psychological distress symptoms and/or alcohol use may put individuals at increased risk of being victims of sexual harassment. Data from more than 2,000 college students in the Midwestern United States were used to examine reciprocal causal effects of the relations between (a) experiencing sexual harassment and alcohol use and (b) experiencing sexual harassment and psychological distress symptoms, specifically depression and anger/hostility. Analyses were conducted separately for sexual harassment which occurs at school and which occurs in college students' workplaces, and also separately for men and women. Results of cross-lagged panel models showed that there were reciprocal causal effects between sexual harassment and alcohol problems, depression, and anger. Discussion focuses on the overall patterns of results as well as the nuances within these findings.
Sexual Harassment, Psychological Distress, and Problematic Drinking Behavior among College Students: An Examination of Reciprocal Causal Relations

PubMed Central

Wolff, Jennifer M.; Rospenda, Kathleen M.; Colaneri, Anthony S.

2016-01-01

Sexual harassment on college campuses is a frequent occurrence and serious public health concern. Victims of sexual harassment are at risk for many possible negative health consequences. In addition, certain psychological distress symptoms and/or alcohol use may put individuals at increased risk of being victims of sexual harassment. Data from over 2,000 college students in the Midwestern United States were used to examine reciprocal causal effects of the relations between (1) experiencing sexual harassment and alcohol use and (2) experiencing sexual harassment and psychological distress symptoms, specifically depression and anger/hostility. Analyses were conducted separately for sexual harassment that occurs at school and that occurs in college students’ workplaces and also separately for men and women. Results of cross-lagged panel models showed that there were reciprocal causal effects between sexual harassment and alcohol problems, depression, and anger. Discussion focuses on the overall patterns of results as well as the nuances within these findings. PMID:26983588
Causal Mathematical Logic as a guiding framework for the prediction of "Intelligence Signals" in brain simulations

NASA Astrophysics Data System (ADS)

Lanzalaco, Felix; Pissanetzky, Sergio

2013-12-01

A recent theory of physical information based on the fundamental principles of causality and thermodynamics has proposed that a large number of observable life and intelligence signals can be described in terms of the Causal Mathematical Logic (CML), which is proposed to encode the natural principles of intelligence across any physical domain and substrate. We attempt to expound the current definition of CML, the "Action functional" as a theory in terms of its ability to possess a superior explanatory power for the current neuroscientific data we use to measure the mammalian brains "intelligence" processes at its most general biophysical level. Brain simulation projects define their success partly in terms of the emergence of "non-explicitly programmed" complex biophysical signals such as self-oscillation and spreading cortical waves. Here we propose to extend the causal theory to predict and guide the understanding of these more complex emergent "intelligence Signals". To achieve this we review whether causal logic is consistent with, can explain and predict the function of complete perceptual processes associated with intelligence. Primarily those are defined as the range of Event Related Potentials (ERP) which include their primary subcomponents; Event Related Desynchronization (ERD) and Event Related Synchronization (ERS). This approach is aiming for a universal and predictive logic for neurosimulation and AGi. The result of this investigation has produced a general "Information Engine" model from translation of the ERD and ERS. The CML algorithm run in terms of action cost predicts ERP signal contents and is consistent with the fundamental laws of thermodynamics. A working substrate independent natural information logic would be a major asset. An information theory consistent with fundamental physics can be an AGi. It can also operate within genetic information space and provides a roadmap to understand the live biophysical operation of the phenotype
Implications of the causality principle for ultra chiral metamaterials

PubMed Central

Gorkunov, Maxim V.; Dmitrienko, Vladimir E.; Ezhov, Alexander A.; Artemov, Vladimir V.; Rogov, Oleg Y.

2015-01-01

Chiral metamaterials – artificial subwavelength structures with broken mirror symmetry – demonstrate outstanding degree of optical chirality that exhibits sophisticated spectral behavior and can eventually reach extreme values. Based on the fundamental causality principle we show how one can unambiguously relate the metamaterial circular dichroism and optical activity by the generalized Kramers-Kronig relations. Contrary to the conventional relations, the generalized ones provide a unique opportunity of extracting information on material-dependent zeroes of transmission coefficient in the upper half plane of complex frequency. We illustrate the merit of the formulated relations by applying them to the observed ultra chiral optical transmission spectra of subwavelength arrays of chiral holes in silver films. Apart from the possibility of precise verification of experimental data, the relations enable resolving complex eigenfrequencies of metamaterial intrinsic modes and resonances. PMID:25787007
Exploring the causal machinery behind sex ratios at birth: does hepatitis B play a role?

PubMed

Hamoudi, Amar

2010-01-01

The causal machinery underlying sex determination is directly relevant to many questions relating gender and family composition to social and economic outcomes. In recent work, Oster highlighted a correlation between parental hepatitis B carrier status and sex of the child. One of her analyses went further, speaking directly to causality. That analysis appeared to have answered an important question that had remained unresolved in medical and biological literatures—namely, does chronic infection with hepatitis B cause male‐skewed sex ratios at birth? Oster’s creative empirical analysis appeared to suggest that it does; however, in this article I reassess the result and present evidence that, at the very least, the question remains open. Further investigation into questions around the causal machinery of sex determination is warranted in the social science literature, as well as in that of biology and medicine. However, my results suggest that it is extremely unlikely that chronic hepatitis B infection plays a biologically significant role.

Identifying causal linkages between environmental variables and African conflicts

NASA Astrophysics Data System (ADS)

Nguy-Robertson, A. L.; Dartevelle, S.

2017-12-01

Environmental variables that contribute to droughts, flooding, and other natural hazards are often identified as factors contributing to conflict; however, few studies attempt to quantify these causal linkages. Recent research has demonstrated that the environment operates within a dynamical system framework and the influence of variables can be identified from convergent cross mapping (CCM) between shadow manifolds. We propose to use CCM to identify causal linkages between environmental variables and incidences of conflict. This study utilizes time series data from Climate Forecast System ver. 2 and MODIS satellite sensors processed using Google Earth Engine to aggregate country and regional trends. These variables are then compared to Armed Conflict Location & Event Data Project observations at similar scales. Results provide relative rankings of variables and their linkage to conflict. Being able to identify which factors contributed more strongly to a conflict can allow policy makers to prepare solutions to mitigate future crises. Knowledge of the primary environmental factors can lead to the identification of other variables to examine in the causal network influencing conflict.
Darwin, Veblen and the problem of causality in economics.

PubMed

Hodgson, G M

2001-01-01

This article discusses some of the ways in which Darwinism has influenced a small minority of economists. It is argued that Darwinism involves a philosophical as well as a theoretical doctrine. Despite claims to the contrary, the uses of analogies to Darwinian natural selection theory are highly limited in economics. Exceptions include Thorstein Veblen, Richard Nelson, and Sidney Winter. At the philosophical level, one of the key features of Darwinism is its notion of detailed understanding in terms of chains of cause and effect. This issue is discussed in the context of the problem of causality in social theory. At least in Darwinian terms, the prevailing causal dualism--of intentional and mechanical causality--in the social sciences is found wanting. Once again, Veblen was the first economist to understand the implications for economics of Darwinism at this philosophical level. For Veblen, it was related to his notion of 'cumulative causation'. The article concludes with a discussion of the problems and potential of this Veblenian position.
Bulk viscosity and relaxation time of causal dissipative relativistic fluid dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang Xuguang; Rischke, Dirk H.; Institut fuer Theoretische Physik, J.W. Goethe-Universitaet, D-60438 Frankfurt am Main

2011-02-15

The microscopic formulas of the bulk viscosity {zeta} and the corresponding relaxation time {tau}{sub {Pi}} in causal dissipative relativistic fluid dynamics are derived by using the projection operator method. In applying these formulas to the pionic fluid, we find that the renormalizable energy-momentum tensor should be employed to obtain consistent results. In the leading-order approximation in the chiral perturbation theory, the relaxation time is enhanced near the QCD phase transition, and {tau}{sub {Pi}} and {zeta} are related as {tau}{sub {Pi}={zeta}}/[{beta}{l_brace}(1/3-c{sub s}{sup 2})({epsilon}+P)-2({epsilon}-3P)/9{r_brace}], where {epsilon}, P, and c{sub s} are the energy density, pressure, and velocity of sound, respectively. The predictedmore » {zeta} and {tau}{sub {Pi}} should satisfy the so-called causality condition. We compare our result with the results of the kinetic calculation by Israel and Stewart and the string theory, and confirm that all three approaches are consistent with the causality condition.« less
Assessing causality in drug policy analyses: How useful are the Bradford Hill criteria in analysing take-home naloxone programs?

PubMed

Olsen, Anna; McDonald, David; Lenton, Simon; Dietze, Paul M

2018-05-01

The Bradford Hill criteria for assessing causality are useful in assembling evidence, including within complex policy analyses. In this paper, we argue that the implementation of take-home naloxone (THN) programs in Australia and elsewhere reflects sensible, evidence-based public health policy, despite the absence of randomised controlled trials. However, we also acknowledge that the debate around expanding access to THN would benefit from a careful consideration of causal inference and health policy impact of THN program implementation. Given the continued debate around expanding access to THN, and the relatively recent access to new data from implementation studies, two research groups independently conducted Bradford Hill analyses in order to carefully consider causal inference and health policy impact. Hill's criteria offer a useful analytical tool for interpreting current evidence on THN programs and making decisions about the (un)certainty of THN program safety and effectiveness. © 2017 Australasian Professional Society on Alcohol and other Drugs.
Selective Entrainment of Theta Oscillations in the Dorsal Stream Causally Enhances Auditory Working Memory Performance.

PubMed

Albouy, Philippe; Weiss, Aurélien; Baillet, Sylvain; Zatorre, Robert J

2017-04-05

The implication of the dorsal stream in manipulating auditory information in working memory has been recently established. However, the oscillatory dynamics within this network and its causal relationship with behavior remain undefined. Using simultaneous MEG/EEG, we show that theta oscillations in the dorsal stream predict participants' manipulation abilities during memory retention in a task requiring the comparison of two patterns differing in temporal order. We investigated the causal relationship between brain oscillations and behavior by applying theta-rhythmic TMS combined with EEG over the MEG-identified target (left intraparietal sulcus) during the silent interval between the two stimuli. Rhythmic TMS entrained theta oscillation and boosted participants' accuracy. TMS-induced oscillatory entrainment scaled with behavioral enhancement, and both gains varied with participants' baseline abilities. These effects were not seen for a melody-comparison control task and were not observed for arrhythmic TMS. These data establish theta activity in the dorsal stream as causally related to memory manipulation. VIDEO ABSTRACT. Copyright © 2017 Elsevier Inc. All rights reserved.
Bell's local causality, Leggett's crypto-nonlocality, and quantum separability are genuinely different concepts

NASA Astrophysics Data System (ADS)

Branciard, Cyril

2013-10-01

I clarify here the relation between Leggett's concept of crypto-nonlocality and the better known notions of Bell's local causality and quantum separability, emphasizing that these are three genuinely different concepts. In particular, I show that while the correlations of separable quantum states clearly satisfy the assumptions of crypto-nonlocality, the opposite is not true: there exist entangled states whose correlations are always compatible with Leggett's crypto-nonlocality.
Causal Relation Analysis Tool of the Case Study in the Engineer Ethics Education

NASA Astrophysics Data System (ADS)

Suzuki, Yoshio; Morita, Keisuke; Yasui, Mitsukuni; Tanada, Ichirou; Fujiki, Hiroyuki; Aoyagi, Manabu

In engineering ethics education, the virtual experiencing of dilemmas is essential. Learning through the case study method is a particularly effective means. Many case studies are, however, difficult to deal with because they often include many complex causal relationships and social factors. It would thus be convenient if there were a tool that could analyze the factors of a case example and organize them into a hierarchical structure to get a better understanding of the whole picture. The tool that was developed applies a cause-and-effect matrix and simple graph theory. It analyzes the causal relationship between facts in a hierarchical structure and organizes complex phenomena. The effectiveness of this tool is shown by presenting an actual example.
Exploring the future with anticipatory networks

NASA Astrophysics Data System (ADS)

Skulimowski, A. M. J.

2013-01-01

This paper presents a theory of anticipatory networks that originates from anticipatory models of consequences in multicriteria decision problems. When making a decision, the decision maker takes into account the anticipated outcomes of each future decision problem linked by the causal relations with the present one. In a network of linked decision problems, the causal relations are defined between time-ordered nodes. The scenarios of future consequences of each decision are modeled by multiple vertices starting from an appropriate node. The network is supplemented by one or more relations of anticipation, or future feedback, which describe a situation where decision makers take into account the anticipated results of some future optimization problems while making their choice. So arises a multigraph of decision problems linked causally and by one or more anticipation relation, termed here the anticipatory network. We will present the properties of anticipatory networks and propose a method of reducing, transforming and using them to solve current decision problems. Furthermore, it will be shown that most anticipatory networks can be regarded as superanticipatory systems, i.e. systems that are anticipatory in the Rosen sense and contain a future model of at least one other anticipatory system. The anticipatory networks can also be applied to filter the set of future scenarios in a foresight exercise.
U(1) current from the AdS/CFT: diffusion, conductivity and causality

NASA Astrophysics Data System (ADS)

Bu, Yanyan; Lublinsky, Michael; Sharon, Amir

2016-04-01

For a holographically defined finite temperature theory, we derive an off-shell constitutive relation for a global U(1) current driven by a weak external non-dynamical electromagnetic field. The constitutive relation involves an all order gradient expansion resummed into three momenta-dependent transport coefficient functions: diffusion, electric conductivity, and "magnetic" conductivity. These transport functions are first computed analytically in the hydrodynamic limit, up to third order in the derivative expansion, and then numerically for generic values of momenta. We also compute a diffusion memory function, which, as a result of all order gradient resummation, is found to be causal.
Moving towards causality in attention-deficit hyperactivity disorder: overview of neural and genetic mechanisms

PubMed Central

Gallo, Eduardo F; Posner, Jonathan

2016-01-01

Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterised by developmentally inappropriate levels of inattention and hyperactivity or impulsivity. The heterogeneity of its clinical manifestations and the differential responses to treatment and varied prognoses have long suggested myriad underlying causes. Over the past decade, clinical and basic research efforts have uncovered many behavioural and neurobiological alterations associated with ADHD, from genes to higher order neural networks. Here, we review the neurobiology of ADHD by focusing on neural circuits implicated in the disorder and discuss how abnormalities in circuitry relate to symptom presentation and treatment. We summarise the literature on genetic variants that are potentially related to the development of ADHD, and how these, in turn, might affect circuit function and relevant behaviours. Whether these underlying neurobiological factors are causally related to symptom presentation remains unresolved. Therefore, we assess efforts aimed at disentangling issues of causality, and showcase the shifting research landscape towards endophenotype refinement in clinical and preclinical settings. Furthermore, we review approaches being developed to understand the neurobiological underpinnings of this complex disorder including the use of animal models, neuromodulation, and pharmaco-imaging studies. PMID:27183902
Causal Inference for fMRI Time Series Data with Systematic Errors of Measurement in a Balanced On/Off Study of Social Evaluative Threat.

PubMed

Sobel, Michael E; Lindquist, Martin A

2014-07-01

Functional magnetic resonance imaging (fMRI) has facilitated major advances in understanding human brain function. Neuroscientists are interested in using fMRI to study the effects of external stimuli on brain activity and causal relationships among brain regions, but have not stated what is meant by causation or defined the effects they purport to estimate. Building on Rubin's causal model, we construct a framework for causal inference using blood oxygenation level dependent (BOLD) fMRI time series data. In the usual statistical literature on causal inference, potential outcomes, assumed to be measured without systematic error, are used to define unit and average causal effects. However, in general the potential BOLD responses are measured with stimulus dependent systematic error. Thus we define unit and average causal effects that are free of systematic error. In contrast to the usual case of a randomized experiment where adjustment for intermediate outcomes leads to biased estimates of treatment effects (Rosenbaum, 1984), here the failure to adjust for task dependent systematic error leads to biased estimates. We therefore adjust for systematic error using measured "noise covariates" , using a linear mixed model to estimate the effects and the systematic error. Our results are important for neuroscientists, who typically do not adjust for systematic error. They should also prove useful to researchers in other areas where responses are measured with error and in fields where large amounts of data are collected on relatively few subjects. To illustrate our approach, we re-analyze data from a social evaluative threat task, comparing the findings with results that ignore systematic error.
A hierarchical causal taxonomy of psychopathology across the life span.

PubMed

Lahey, Benjamin B; Krueger, Robert F; Rathouz, Paul J; Waldman, Irwin D; Zald, David H

2017-02-01

We propose a taxonomy of psychopathology based on patterns of shared causal influences identified in a review of multivariate behavior genetic studies that distinguish genetic and environmental influences that are either common to multiple dimensions of psychopathology or unique to each dimension. At the phenotypic level, first-order dimensions are defined by correlations among symptoms; correlations among first-order dimensions similarly define higher-order domains (e.g., internalizing or externalizing psychopathology). We hypothesize that the robust phenotypic correlations among first-order dimensions reflect a hierarchy of increasingly specific etiologic influences . Some nonspecific etiologic factors increase risk for all first-order dimensions of psychopathology to varying degrees through a general factor of psychopathology. Other nonspecific etiologic factors increase risk only for all first-order dimensions within a more specific higher-order domain. Furthermore, each first-order dimension has its own unique causal influences. Genetic and environmental influences common to family members tend to be nonspecific, whereas environmental influences unique to each individual are more dimension-specific. We posit that these causal influences on psychopathology are moderated by sex and developmental processes. This causal taxonomy also provides a novel framework for understanding the heterogeneity of each first-order dimension: Different persons exhibiting similar symptoms may be influenced by different combinations of etiologic influences from each of the 3 levels of the etiologic hierarchy. Furthermore, we relate the proposed causal taxonomy to transdimensional psychobiological processes, which also impact the heterogeneity of each psychopathology dimension. This causal taxonomy implies the need for changes in strategies for studying the etiology, psychobiology, prevention, and treatment of psychopathology. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Too much sitting and all-cause mortality: is there a causal link?

PubMed

Biddle, Stuart J H; Bennie, Jason A; Bauman, Adrian E; Chau, Josephine Y; Dunstan, David; Owen, Neville; Stamatakis, Emmanuel; van Uffelen, Jannique G Z

2016-07-26

Sedentary behaviours (time spent sitting, with low energy expenditure) are associated with deleterious health outcomes, including all-cause mortality. Whether this association can be considered causal has yet to be established. Using systematic reviews and primary studies from those reviews, we drew upon Bradford Hill's criteria to consider the likelihood that sedentary behaviour in epidemiological studies is likely to be causally related to all-cause (premature) mortality. Searches for systematic reviews on sedentary behaviours and all-cause mortality yielded 386 records which, when judged against eligibility criteria, left eight reviews (addressing 17 primary studies) for analysis. Exposure measures included self-reported total sitting time, TV viewing time, and screen time. Studies included comparisons of a low-sedentary reference group with several higher sedentary categories, or compared the highest versus lowest sedentary behaviour groups. We employed four Bradford Hill criteria: strength of association, consistency, temporality, and dose-response. Evidence supporting causality at the level of each systematic review and primary study was judged using a traffic light system depicting green for causal evidence, amber for mixed or inconclusive evidence, and red for no evidence for causality (either evidence of no effect or no evidence reported). The eight systematic reviews showed evidence for consistency (7 green) and temporality (6 green), and some evidence for strength of association (4 green). There was no evidence for a dose-response relationship (5 red). Five reviews were rated green overall. Twelve (67 %) of the primary studies were rated green, with evidence for strength and temporality. There is reasonable evidence for a likely causal relationship between sedentary behaviour and all-cause mortality based on the epidemiological criteria of strength of association, consistency of effect, and temporality.
Granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems.

PubMed

Zhou, Douglas; Xiao, Yanyang; Zhang, Yaoyu; Xu, Zhiqin; Cai, David

2014-01-01

Reconstruction of anatomical connectivity from measured dynamical activities of coupled neurons is one of the fundamental issues in the understanding of structure-function relationship of neuronal circuitry. Many approaches have been developed to address this issue based on either electrical or metabolic data observed in experiment. The Granger causality (GC) analysis remains one of the major approaches to explore the dynamical causal connectivity among individual neurons or neuronal populations. However, it is yet to be clarified how such causal connectivity, i.e., the GC connectivity, can be mapped to the underlying anatomical connectivity in neuronal networks. We perform the GC analysis on the conductance-based integrate-and-fire (I&F) neuronal networks to obtain their causal connectivity. Through numerical experiments, we find that the underlying synaptic connectivity amongst individual neurons or subnetworks, can be successfully reconstructed by the GC connectivity constructed from voltage time series. Furthermore, this reconstruction is insensitive to dynamical regimes and can be achieved without perturbing systems and prior knowledge of neuronal model parameters. Surprisingly, the synaptic connectivity can even be reconstructed by merely knowing the raster of systems, i.e., spike timing of neurons. Using spike-triggered correlation techniques, we establish a direct mapping between the causal connectivity and the synaptic connectivity for the conductance-based I&F neuronal networks, and show the GC is quadratically related to the coupling strength. The theoretical approach we develop here may provide a framework for examining the validity of the GC analysis in other settings.
Granger Causality Network Reconstruction of Conductance-Based Integrate-and-Fire Neuronal Systems

PubMed Central

Zhou, Douglas; Xiao, Yanyang; Zhang, Yaoyu; Xu, Zhiqin; Cai, David

2014-01-01

Reconstruction of anatomical connectivity from measured dynamical activities of coupled neurons is one of the fundamental issues in the understanding of structure-function relationship of neuronal circuitry. Many approaches have been developed to address this issue based on either electrical or metabolic data observed in experiment. The Granger causality (GC) analysis remains one of the major approaches to explore the dynamical causal connectivity among individual neurons or neuronal populations. However, it is yet to be clarified how such causal connectivity, i.e., the GC connectivity, can be mapped to the underlying anatomical connectivity in neuronal networks. We perform the GC analysis on the conductance-based integrate-and-fire (IF) neuronal networks to obtain their causal connectivity. Through numerical experiments, we find that the underlying synaptic connectivity amongst individual neurons or subnetworks, can be successfully reconstructed by the GC connectivity constructed from voltage time series. Furthermore, this reconstruction is insensitive to dynamical regimes and can be achieved without perturbing systems and prior knowledge of neuronal model parameters. Surprisingly, the synaptic connectivity can even be reconstructed by merely knowing the raster of systems, i.e., spike timing of neurons. Using spike-triggered correlation techniques, we establish a direct mapping between the causal connectivity and the synaptic connectivity for the conductance-based IF neuronal networks, and show the GC is quadratically related to the coupling strength. The theoretical approach we develop here may provide a framework for examining the validity of the GC analysis in other settings. PMID:24586285
Quantifying causal emergence shows that macro can beat micro.

PubMed

Hoel, Erik P; Albantakis, Larissa; Tononi, Giulio

2013-12-03

Causal interactions within complex systems can be analyzed at multiple spatial and temporal scales. For example, the brain can be analyzed at the level of neurons, neuronal groups, and areas, over tens, hundreds, or thousands of milliseconds. It is widely assumed that, once a micro level is fixed, macro levels are fixed too, a relation called supervenience. It is also assumed that, although macro descriptions may be convenient, only the micro level is causally complete, because it includes every detail, thus leaving no room for causation at the macro level. However, this assumption can only be evaluated under a proper measure of causation. Here, we use a measure [effective information (EI)] that depends on both the effectiveness of a system's mechanisms and the size of its state space: EI is higher the more the mechanisms constrain the system's possible past and future states. By measuring EI at micro and macro levels in simple systems whose micro mechanisms are fixed, we show that for certain causal architectures EI can peak at a macro level in space and/or time. This happens when coarse-grained macro mechanisms are more effective (more deterministic and/or less degenerate) than the underlying micro mechanisms, to an extent that overcomes the smaller state space. Thus, although the macro level supervenes upon the micro, it can supersede it causally, leading to genuine causal emergence--the gain in EI when moving from a micro to a macro level of analysis.
Belief beyond the evidence: using the proposed effect of breakfast on obesity to show 2 practices that distort scientific evidence1234

PubMed Central

Brown, Andrew W; Bohan Brown, Michelle M

2013-01-01

Background: Various intentional and unintentional factors influence beliefs beyond what scientific evidence justifies. Two such factors are research lacking probative value (RLPV) and biased research reporting (BRR). Objective: We investigated the prevalence of RLPV and BRR in research about the proposition that skipping breakfast causes weight gain, which is called the proposed effect of breakfast on obesity (PEBO) in this article. Design: Studies related to the PEBO were synthesized by using a cumulative meta-analysis. Abstracts from these studies were also rated for the improper use of causal language and biased interpretations. In separate analyses, articles that cited an observational study about the PEBO were rated for the inappropriate use of causal language, and articles that cited a randomized controlled trial (RCT) about the PEBO were rated for misleadingly citing the RCT. Results: The current body of scientific knowledge indicates that the PEBO is only presumed true. The observational literature on the PEBO has gratuitously established the association, but not the causal relation, between skipping breakfast and obesity (final cumulative meta-analysis P value <10−42), which is evidence of RLPV. Four examples of BRR are evident in the PEBO literature as follows: 1) biased interpretation of one's own results, 2) improper use of causal language in describing one's own results, 3) misleadingly citing others’ results, and 4) improper use of causal language in citing others’ work. Conclusions: The belief in the PEBO exceeds the strength of scientific evidence. The scientific record is distorted by RLPV and BRR. RLPV is a suboptimal use of collective scientific resources. PMID:24004890
Contours of a causal feedback mechanism between adaptive personality and psychosocial function in patients with personality disorders: a secondary analysis from a randomized clinical trial.

PubMed

Klungsøyr, Ole; Antonsen, Bjørnar; Wilberg, Theresa

2017-06-05

Patients with personality disorders commonly exhibit impairment in psychosocial function that persists over time even with diagnostic remission. Further causal knowledge may help to identify and assess factors with a potential to alleviate this impairment. Psychosocial function is associated with personality functioning which describes personality disorder severity in DSM-5 (section III) and which can reportedly be improved by therapy. The reciprocal association between personality functioning and psychosocial function was assessed, in 113 patients with different personality disorders, in a secondary longitudinal analysis of data from a randomized clinical trial, over six years. Personality functioning was represented by three domains of the Severity Indices of Personality Problems: Relational Capacity, Identity Integration, and Self-control. Psychosocial function was measured by Global Assessment of Functioning. The marginal structural model was used for estimation of causal effects of the three personality functioning domains on psychosocial function, and vice versa. The attractiveness of this model lies in the ability to assess an effect of a time - varying exposure on an outcome, while adjusting for time - varying confounding. Strong causal effects were found. A hypothetical intervention to increase Relational Capacity by one standard deviation, both at one and two time-points prior to assessment of psychosocial function, would increase psychosocial function by 3.5 standard deviations (95% CI: 2.0, 4.96). Significant effects of Identity Integration and Self-control on psychosocial function, and from psychosocial function on all three domains of personality functioning, although weaker, were also found. This study indicates that persistent impairment in psychosocial function can be addressed through a causal pathway of personality functioning, with interventions of at least 18 months duration.
Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach.

PubMed

Bollegala, Danushka; Maskell, Simon; Sloane, Richard; Hajne, Joanna; Pirmohamed, Munir

2018-05-09

Detecting adverse drug reactions (ADRs) is an important task that has direct implications for the use of that drug. If we can detect previously unknown ADRs as quickly as possible, then this information can be provided to the regulators, pharmaceutical companies, and health care organizations, thereby potentially reducing drug-related morbidity and saving lives of many patients. A promising approach for detecting ADRs is to use social media platforms such as Twitter and Facebook. A high level of correlation between a drug name and an event may be an indication of a potential adverse reaction associated with that drug. Although numerous association measures have been proposed by the signal detection community for identifying ADRs, these measures are limited in that they detect correlations but often ignore causality. This study aimed to propose a causality measure that can detect an adverse reaction that is caused by a drug rather than merely being a correlated signal. To the best of our knowledge, this was the first causality-sensitive approach for detecting ADRs from social media. Specifically, the relationship between a drug and an event was represented using a set of automatically extracted lexical patterns. We then learned the weights for the extracted lexical patterns that indicate their reliability for expressing an adverse reaction of a given drug. Our proposed method obtains an ADR detection accuracy of 74% on a large-scale manually annotated dataset of tweets, covering a standard set of drugs and adverse reactions. By using lexical patterns, we can accurately detect the causality between drugs and adverse reaction-related events. ©Danushka Bollegala, Simon Maskell, Richard Sloane, Joanna Hajne, Munir Pirmohamed. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 09.05.2018.
Arsenic metabolism efficiency has a causal role in arsenic toxicity: Mendelian randomization and gene-environment interaction.

PubMed

Pierce, Brandon L; Tong, Lin; Argos, Maria; Gao, Jianjun; Farzana, Jasmine; Roy, Shantanu; Paul-Brutus, Rachelle; Rahaman, Ronald; Rakibuz-Zaman, Muhammad; Parvez, Faruque; Ahmed, Alauddin; Quasem, Iftekhar; Hore, Samar K; Alam, Shafiul; Islam, Tariqul; Harjes, Judith; Sarwar, Golam; Slavkovich, Vesna; Gamble, Mary V; Chen, Yu; Yunus, Mohammad; Rahman, Mahfuzar; Baron, John A; Graziano, Joseph H; Ahsan, Habibul

2013-12-01

Arsenic exposure through drinking water is a serious global health issue. Observational studies suggest that individuals who metabolize arsenic efficiently are at lower risk for toxicities such as arsenical skin lesions. Using two single nucleotide polymorphisms(SNPs) in the 10q24.32 region (near AS3MT) that show independent associations with metabolism efficiency, Mendelian randomization can be used to assess whether the association between metabolism efficiency and skin lesions is likely to be causal. Using data on 2060 arsenic-exposed Bangladeshi individuals, we estimated associations for two 10q24.32 SNPs with relative concentrations of three urinary arsenic species (representing metabolism efficiency): inorganic arsenic (iAs), monomethylarsonic acid(MMA) and dimethylarsinic acid (DMA). SNP-based predictions of iAs%, MMA% and DMA% were tested for association with skin lesion status among 2483 cases and 2857 controls. Causal odds ratios for skin lesions were 0.90 (95% confidence interval[CI]: 0.87, 0.95), 1.19 (CI: 1.10, 1.28) and 1.23 (CI: 1.12, 1.36)for a one standard deviation increase in DMA%, MMA% and iAs%,respectively. We demonstrated genotype-arsenic interaction, with metabolism-related variants showing stronger associations with skin lesion risk among individuals with high arsenic exposure (synergy index: 1.37; CI: 1.11, 1.62). We provide strong evidence for a causal relationship between arsenic metabolism efficiency and skin lesion risk. Mendelian randomization can be used to assess the causal role of arsenic exposure and metabolism in a wide array of health conditions.exposure and metabolism in a wide array of health conditions.Developing interventions that increase arsenic metabolism efficiency are likely to reduce the impact of arsenic exposure on health.

Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment

ERIC Educational Resources Information Center

Johnson, Samuel G. B.; Ahn, Woo-kyoung

2015-01-01

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge--an interconnected causal "network," where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms--causal "islands"--such that events in different…
Occams Quantum Strop: Synchronizing and Compressing Classical Cryptic Processes via a Quantum Channel (Open Source)

DTIC Science & Technology

2016-02-15

do not quote them here. A sequel details a yet more efficient analytic technique based on holomorphic functions of the internal - state Markov chain...required, though, when synchronizing over a quantum channel? Recent work demonstrated that representing causal similarity as quantum state ...minimal, unifilar predictor4. The -machine’s causal states σ ∈ are defined by the equivalence relation that groups all histories = −∞ ←x x :0 that
Complementarity in the multiverse

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bousso, Raphael

2009-06-15

In the multiverse, as in AdS space, light cones relate bulk points to boundary scales. This holographic UV-IR connection defines a preferred global time cutoff that regulates the divergences of eternal inflation. An entirely different cutoff, the causal patch, arises in the holographic description of black holes. Remarkably, I find evidence that these two regulators define the same probability measure in the multiverse. Initial conditions for the causal patch are controlled by the late-time attractor regime of the global description.
Why cachexia kills: examining the causality of poor outcomes in wasting conditions.

PubMed

Kalantar-Zadeh, Kamyar; Rhee, Connie; Sim, John J; Stenvinkel, Peter; Anker, Stefan D; Kovesdy, Csaba P

2013-06-01

Weight loss is the hallmark of any progressive acute or chronic disease state. In its extreme form of significant lean body mass (including skeletal muscle) and fat loss, it is referred to as cachexia. It has been known for millennia that muscle and fat wasting leads to poor outcomes including death. On one hand, conditions and risk factors that lead to cachexia and inadequate nutrition may independently lead to increased mortality. Additionaly, cachexia per se, withdrawal of nutritional support in progressive cachexia, and advanced age may lead to death via cachexia-specific pathways. Despite the strong and consistent association of cachexia with mortality, no unifying mechanism has yet been suggested as to why wasting conditions are associated with an exceptionally high mortality risk. Hence, the causality of the cachexia-death association, even though it is biologically plausible, is widely unknown. This century-long uncertainty may have played a role as to why the field of cachexia treatment development has not shown major advances over the past decades. We suggest that cachexia-associated relative thrombocytosis and platelet activation may play a causal role in cachexia-related death, while other mechanisms may also contribute including arrhythmia-associated sudden deaths, endocrine disorders such as hypothyroidism, and immune system compromise leading to infectious events and deaths. Multidimensional research including examining biologically plausible models is urgently needed to investigate the causality of the cachexia-death association.
A Analysis of the Development of Weather Concepts

NASA Astrophysics Data System (ADS)

Mroz, Paul John

Weather information in all forms is poorly understood and often misinterpreted by the general public. Weather literacy is necessary for everyone if critical weather messages, designed to save lives and protect property, are to be effective. The purpose of this study was to seek content and causal evidence for a developmental concept of Weather Information Processing that was consistent with Piagetian Cognitive Stages of Development. Three ordinal Content Stages Of Weather Information Processing (phenomena, process and mechanism) and three ordinal Causal Explanation Stages Of Weather Information Processing (non-real, natural, and scientifically valid abstract ideas) were explored for their relationship with Piaget's Pre-Operational, Concrete and Formal Stages of Development. One hundred and fifty -five elementary and secondary school students from two school districts were administered a written Piagetian exam. Commonly available television weather programs were categorized, randomly assigned and viewed by 42 randomly selected students who were administered three Piagetian tasks. Students were clinically interviewed for the level of content information and causal explanations (reasoning). Results indicated that content information and causal reasoning of students to televised weather information is significantly related (p <.01) to age, and Piagetian Cognitive Stages of Development. Two Piagetian logic operations (seriation and correlation) were established as significantly different (p <.05) when related to age. These findings support a developmental concept of Weather Information Processing and have implications for teaching and presenting weather information to the public.
Simple exposure to alcohol cues causally increases negative implicit attitudes toward lesbians and gay men.

PubMed

Greitemeyer, Tobias; Nierula, Carina

2016-01-01

Previous research has shown that acute alcohol consumption is associated with negative responses toward outgroup members such as sexual minorities. However, simple alcohol cue exposure without actually consuming alcohol also influences social behavior. Hence, it was reasoned that priming participants with words related to alcohol (relative to neutral words) would promote prejudiced attitudes toward sexual minorities. In fact, an experiment showed that alcohol cue exposure causally led to more negative implicit attitudes toward lesbians and gay men. In contrast, participants' explicit attitudes were relatively unaffected by the priming manipulation. Moreover, participants' typical alcohol use was not related to their attitudes toward lesbians and gay men. In sum, it appears that not only acute alcohol consumption but also the simple exposure of alcohol cues may promote negative views toward lesbians and gay men.
Quantum computation with indefinite causal structures

NASA Astrophysics Data System (ADS)

Araújo, Mateus; Guérin, Philippe Allard; Baumeler, ńmin

2017-11-01

One way to study the physical plausibility of closed timelike curves (CTCs) is to examine their computational power. This has been done for Deutschian CTCs (D-CTCs) and postselection CTCs (P-CTCs), with the result that they allow for the efficient solution of problems in PSPACE and PP, respectively. Since these are extremely powerful complexity classes, which are not expected to be solvable in reality, this can be taken as evidence that these models for CTCs are pathological. This problem is closely related to the nonlinearity of this models, which also allows, for example, cloning quantum states, in the case of D-CTCs, or distinguishing nonorthogonal quantum states, in the case of P-CTCs. In contrast, the process matrix formalism allows one to model indefinite causal structures in a linear way, getting rid of these effects and raising the possibility that its computational power is rather tame. In this paper, we show that process matrices correspond to a linear particular case of P-CTCs, and therefore that its computational power is upperbounded by that of PP. We show, furthermore, a family of processes that can violate causal inequalities but nevertheless can be simulated by a causally ordered quantum circuit with only a constant overhead, showing that indefinite causality is not necessarily hard to simulate.
On the interpretability and computational reliability of frequency-domain Granger causality.

PubMed

Faes, Luca; Stramaglia, Sebastiano; Marinazzo, Daniele

2017-01-01

This Correspondence article is a comment which directly relates to the paper "A study of problems encountered in Granger causality analysis from a neuroscience perspective" ( Stokes and Purdon, 2017). We agree that interpretation issues of Granger causality (GC) in neuroscience exist, partially due to the historically unfortunate use of the name "causality", as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, since data from simulated systems are used, the pitfalls that are found with the used formulation are intended to be general, and not limited to neuroscience. It would be a pity if this paper, even if written in good faith, became a wildcard against all possible applications of GC, regardless of the large body of work recently published which aims to address faults in methodology and interpretation. In order to provide a balanced view, we replicate the simulations of Stokes and Purdon, using an updated GC implementation and exploiting the combination of spectral and causal information, showing that in this way the pitfalls are mitigated or directly solved.
Inferring Causalities in Landscape Genetics: An Extension of Wright's Causal Modeling to Distance Matrices.

PubMed

Fourtune, Lisa; Prunier, Jérôme G; Paz-Vinas, Ivan; Loot, Géraldine; Veyssière, Charlotte; Blanchet, Simon

2018-04-01

Identifying landscape features that affect functional connectivity among populations is a major challenge in fundamental and applied sciences. Landscape genetics combines landscape and genetic data to address this issue, with the main objective of disentangling direct and indirect relationships among an intricate set of variables. Causal modeling has strong potential to address the complex nature of landscape genetic data sets. However, this statistical approach was not initially developed to address the pairwise distance matrices commonly used in landscape genetics. Here, we aimed to extend the applicability of two causal modeling methods-that is, maximum-likelihood path analysis and the directional separation test-by developing statistical approaches aimed at handling distance matrices and improving functional connectivity inference. Using simulations, we showed that these approaches greatly improved the robustness of the absolute (using a frequentist approach) and relative (using an information-theoretic approach) fits of the tested models. We used an empirical data set combining genetic information on a freshwater fish species (Gobio occitaniae) and detailed landscape descriptors to demonstrate the usefulness of causal modeling to identify functional connectivity in wild populations. Specifically, we demonstrated how direct and indirect relationships involving altitude, temperature, and oxygen concentration influenced within- and between-population genetic diversity of G. occitaniae.
A Principle of Intentionality.

PubMed

Turner, Charles K

2017-01-01

The mainstream theories and models of the physical sciences, including neuroscience, are all consistent with the principle of causality. Wholly causal explanations make sense of how things go, but are inherently value-neutral, providing no objective basis for true beliefs being better than false beliefs, nor for it being better to intend wisely than foolishly. Dennett (1987) makes a related point in calling the brain a syntactic (procedure-based) engine. He says that you cannot get to a semantic (meaning-based) engine from there. He suggests that folk psychology revolves around an intentional stance that is independent of the causal theories of the brain, and accounts for constructs such as meanings, agency, true belief, and wise desire. Dennett proposes that the intentional stance is so powerful that it can be developed into a valid intentional theory. This article expands Dennett's model into a principle of intentionality that revolves around the construct of objective wisdom. This principle provides a structure that can account for all mental processes, and for the scientific understanding of objective value. It is suggested that science can develop a far more complete worldview with a combination of the principles of causality and intentionality than would be possible with scientific theories that are consistent with the principle of causality alone.
A Principle of Intentionality

PubMed Central

Turner, Charles K.

2017-01-01

The mainstream theories and models of the physical sciences, including neuroscience, are all consistent with the principle of causality. Wholly causal explanations make sense of how things go, but are inherently value-neutral, providing no objective basis for true beliefs being better than false beliefs, nor for it being better to intend wisely than foolishly. Dennett (1987) makes a related point in calling the brain a syntactic (procedure-based) engine. He says that you cannot get to a semantic (meaning-based) engine from there. He suggests that folk psychology revolves around an intentional stance that is independent of the causal theories of the brain, and accounts for constructs such as meanings, agency, true belief, and wise desire. Dennett proposes that the intentional stance is so powerful that it can be developed into a valid intentional theory. This article expands Dennett’s model into a principle of intentionality that revolves around the construct of objective wisdom. This principle provides a structure that can account for all mental processes, and for the scientific understanding of objective value. It is suggested that science can develop a far more complete worldview with a combination of the principles of causality and intentionality than would be possible with scientific theories that are consistent with the principle of causality alone. PMID:28223954
The explanatory structure of unexplainable events: Causal constraints on magical reasoning.

PubMed

Shtulman, Andrew; Morgan, Caitlin

2017-10-01

A common intuition, often captured in fiction, is that some impossible events (e.g., levitating a stone) are "more impossible" than others (e.g., levitating a feather). We investigated the source of this intuition, hypothesizing that graded notions of impossibility arise from explanatory considerations logically precluded by the violation at hand but still taken into account. Studies 1-4 involved college undergraduates (n = 357), and Study 5 involved preschool-aged children (n = 32). In Studies 1 and 2, participants saw pairs of magical spells that violated one of 18 causal principles-six physical, six biological, and six psychological-and were asked to indicate which spell would be more difficult to learn. Both spells violated the same causal principle but differed in their relation to a subsidiary principle. Participants' judgments of spell difficulty honored the subsidiary principle, even when participants were given the option of judging the two spells equally difficult. Study 3 replicated those effects with Likert-type ratings; Study 4 replicated them in an open-ended version of the task in which participants generated their own causal violations; and Study 5 replicated them with children. Taken together, these findings suggest that events that defy causal explanation are interpreted in terms of explanatory considerations that hold in the absence of such violations.
Effects of BMI, Fat Mass, and Lean Mass on Asthma in Childhood: A Mendelian Randomization Study

PubMed Central

Granell, Raquel; Henderson, A. John; Evans, David M.; Smith, George Davey; Ness, Andrew R.; Lewis, Sarah; Palmer, Tom M.; Sterne, Jonathan A. C.

2014-01-01

Background Observational studies have reported associations between body mass index (BMI) and asthma, but confounding and reverse causality remain plausible explanations. We aim to investigate evidence for a causal effect of BMI on asthma using a Mendelian randomization approach. Methods and Findings We used Mendelian randomization to investigate causal effects of BMI, fat mass, and lean mass on current asthma at age 7½ y in the Avon Longitudinal Study of Parents and Children (ALSPAC). A weighted allele score based on 32 independent BMI-related single nucleotide polymorphisms (SNPs) was derived from external data, and associations with BMI, fat mass, lean mass, and asthma were estimated. We derived instrumental variable (IV) estimates of causal risk ratios (RRs). 4,835 children had available data on BMI-associated SNPs, asthma, and BMI. The weighted allele score was strongly associated with BMI, fat mass, and lean mass (all p-values<0.001) and with childhood asthma (RR 2.56, 95% CI 1.38–4.76 per unit score, p = 0.003). The estimated causal RR for the effect of BMI on asthma was 1.55 (95% CI 1.16–2.07) per kg/m2, p = 0.003. This effect appeared stronger for non-atopic (1.90, 95% CI 1.19–3.03) than for atopic asthma (1.37, 95% CI 0.89–2.11) though there was little evidence of heterogeneity (p = 0.31). The estimated causal RRs for the effects of fat mass and lean mass on asthma were 1.41 (95% CI 1.11–1.79) per 0.5 kg and 2.25 (95% CI 1.23–4.11) per kg, respectively. The possibility of genetic pleiotropy could not be discounted completely; however, additional IV analyses using FTO variant rs1558902 and the other BMI-related SNPs separately provided similar causal effects with wider confidence intervals. Loss of follow-up was unlikely to bias the estimated effects. Conclusions Higher BMI increases the risk of asthma in mid-childhood. Higher BMI may have contributed to the increase in asthma risk toward the end of the 20th century. Please see later in the article for the Editors' Summary PMID:24983943
Ponction biopsies rénales dans le Service de Néphrologie de Fès: indications et résultats: à propos de 522 cas

PubMed Central

Mbarki, Houda; Belghiti, Khadija Alaoui; Harmouch, Taoufiq; Najdi, Adil; Arrayhani, Mohamed; Sqalli, Tarik

2016-01-01

L'apport de la ponction biopsie rénale (PBR) dans le diagnostic, le choix thérapeutique et l’évaluation pronostique des néphropathies est considérable. Aucune étude marocaine n'a évalué la pratique et l'apport de la PBR. Notre objectif est d’étudier les indications de la PBR, déterminer la fréquence des maladies rénales identifiées par PBR dans notre région et de faire une confrontation entre les données clinico-biologiques et le diagnostic historique. Notre étude menée entre Janvier 2009 et Décembre 2012, est rétrospective. Nous avons inclus tous les patients du service de Néphrologie du CHU Hassan II de Fès ayant bénéficié d'une biopsie de reins natifs. 522 PBR ont été réalisées. Nous avons exclu 8 biopsies devant le manque de renseignements et avons donc retenu 514. L’âge moyen des patients au moment de la PBR est de 39 ±17 ans (3-82 ans). Le sex ratio est de 0,9. Le syndrome néphrotique est le diagnostic clinique le plus fréquent à tous les âges (58,2%). Les néphropathies glomérulaires représentent 94,2% des maladies rénales diagnostiquées, leur distribution varie selon l’âge des patients. La PBR a confirmé le premier diagnostic suspecté cliniquement dans 40,65% des cas, alors qu'elle a révélé un diagnostic inattendu chez 22,5% d'entre eux. Le diagnostic syndromique permet d'orienter vers la maladie rénale la plus probable et de guider les thérapeutiques urgentes en attendant les résultats de la PBR. Mais il ne peut en aucun remplacer la PBR qui reste le gold standard. PMID:27583085
Sensing the Coherence of Biology in Contrast to Psychology: Young Children’s Use of Causal Relations to Distinguish Two Foundational Domains

PubMed Central

Erickson, Jane E.; Keil, Frank C.; Lockhart, Kristi L.

2011-01-01

To what extent do children understand that biological processes fall into 1 coherent domain unified by distinct causal principles? In Experiments 1 and 2 (N = 125) kindergartners are given triads of biological and psychological processes and asked to identify which 2 members of the triad belong together. Results show that 5-year-olds correctly cluster biological processes and separate them from psychological ones. Experiments 3 and 4 (N = 64) examine whether or not children make this distinction because they understand that biological and psychological processes operate according to fundamentally different causal mechanisms. The results suggest that 5-year-olds do possess this understanding, and furthermore, they have intuitions about the nature of these different mechanisms. PMID:20331675
Impact of military on biofuels consumption and GHG emissions: the evidence from G7 countries.

PubMed

Bildirici, Melike

2018-05-01

It was aimed to test the relation among the greenhouse gases emissions, economic growth, biofuels consumption, and militarization in G7 countries during the 1985-2015 period by Pedroni 1995 and panel Johansen tests and two long-run estimators-dynamic OLS and fully modified OLS. Long-run estimators found that economic growth and militarization have statistically significant positive impact on CO 2 emission of G7 countries. Furthermore, the panel causality tests were applied: Dumitrescu and Hurlin (Econ Model 29(4):1450-1460, 2012) and panel Granger causality. These tests determined the causal relationship between the variables. The results of this paper implied that economic growth and biofuels consumption depend on militarization, and economic growth and militarization are granger causes of the greenhouse gases emissions.
Causal nature of neighborhood deprivation on individual risk of coronary heart disease or ischemic stroke: A prospective national Swedish co-relative control study in men and women.

PubMed

Forsberg, Per-Ola; Ohlsson, Henrik; Sundquist, Kristina

2018-03-01

We studied the association between neighborhood socioeconomic status (SES) and incidence of coronary heart disease (CHD) or ischemic stroke in the total population and in full- and half-siblings to determine whether these associations are causal or a result from familial confounding. Data were retrieved from nationwide Swedish registers containing individual clinical data linked to neighborhood of residence. After adjustment for individual SES, the association between neighborhood SES and CHD showed no decrease with increasing genetic resemblance, particularly in women. This indicates that the association between neighborhood SES and CHD incidence is partially causal among women, which represents a novel finding. Copyright © 2018 Elsevier Ltd. All rights reserved.
Neural reactivations during sleep determine network credit assignment

PubMed Central

Gulati, Tanuj; Guo, Ling; Ramanathan, Dhakshin S.; Bodepudi, Anitha; Ganguly, Karunesh

2018-01-01

A fundamental goal of motor learning is to establish neural patterns that produce a desired behavioral outcome. It remains unclear how and when the nervous system solves this “credit–assignment” problem. Using neuroprosthetic learning where we could control the causal relationship between neurons and behavior, here we show that sleep–dependent processing is required for credit-assignment and the establishment of task-related functional connectivity reflecting the casual neuron-behavior relationship. Importantly, we found a strong link between the microstructure of sleep reactivations and credit assignment, with downscaling of non–causal activity. Strikingly, decoupling of spiking to slow–oscillations using optogenetic methods eliminated rescaling. Thus, our results suggest that coordinated firing during sleep plays an essential role in establishing sparse activation patterns that reflect the causal neuron–behavior relationship. PMID:28692062
Causal diagrams for empirical legal research: a methodology for identifying causation, avoiding bias and interpreting results

PubMed Central

VanderWeele, Tyler J.; Staudt, Nancy

2014-01-01

In this paper we introduce methodology—causal directed acyclic graphs—that empirical researchers can use to identify causation, avoid bias, and interpret empirical results. This methodology has become popular in a number of disciplines, including statistics, biostatistics, epidemiology and computer science, but has yet to appear in the empirical legal literature. Accordingly we outline the rules and principles underlying this new methodology and then show how it can assist empirical researchers through both hypothetical and real-world examples found in the extant literature. While causal directed acyclic graphs are certainly not a panacea for all empirical problems, we show they have potential to make the most basic and fundamental tasks, such as selecting covariate controls, relatively easy and straightforward. PMID:25685055
Causal judgment from contingency information: relation between subjective reports and individual tendencies in judgment.

PubMed

White, P A

2000-04-01

In two experiments, participants made causal judgments from contingency information for problems with different objective contingencies. After the judgment task, the participants reported how their judgments had changed following each type of contingency information. Some reported idiosyncratic tendencies--in other words, tendencies contrary to those expected under associative-learning and normative rule induction models of contingency judgment. These idiosyncratic reports tended to be better predictors of the judgments of those who made them than did the models. The results are consistent with the view that causal judgment from contingency information is made, at least in part, by deliberative use of acquired and sometimes idiosyncratic notions of evidential value, the outcomes of which tend, in aggregate, to be highly correlated with the outcomes of normative procedures.

Incorporating Biological Knowledge into Evaluation of Casual Regulatory Hypothesis

NASA Technical Reports Server (NTRS)

Chrisman, Lonnie; Langley, Pat; Bay, Stephen; Pohorille, Andrew; DeVincenzi, D. (Technical Monitor)

2002-01-01

Biological data can be scarce and costly to obtain. The small number of samples available typically limits statistical power and makes reliable inference of causal relations extremely difficult. However, we argue that statistical power can be increased substantially by incorporating prior knowledge and data from diverse sources. We present a Bayesian framework that combines information from different sources and we show empirically that this lets one make correct causal inferences with small sample sizes that otherwise would be impossible.
Determining Directional Dependency in Causal Associations

PubMed Central

Pornprasertmanit, Sunthud; Little, Todd D.

2014-01-01

Directional dependency is a method to determine the likely causal direction of effect between two variables. This article aims to critique and improve upon the use of directional dependency as a technique to infer causal associations. We comment on several issues raised by von Eye and DeShon (2012), including: encouraging the use of the signs of skewness and excessive kurtosis of both variables, discouraging the use of D’Agostino’s K2, and encouraging the use of directional dependency to compare variables only within time points. We offer improved steps for determining directional dependency that fix the problems we note. Next, we discuss how to integrate directional dependency into longitudinal data analysis with two variables. We also examine the accuracy of directional dependency evaluations when several regression assumptions are violated. Directional dependency can suggest the direction of a relation if (a) the regression error in population is normal, (b) an unobserved explanatory variable correlates with any variables equal to or less than .2, (c) a curvilinear relation between both variables is not strong (standardized regression coefficient ≤ .2), (d) there are no bivariate outliers, and (e) both variables are continuous. PMID:24683282
Changes in the interaction of resting-state neural networks from adolescence to adulthood.

PubMed

Stevens, Michael C; Pearlson, Godfrey D; Calhoun, Vince D

2009-08-01

This study examined how the mutual interactions of functionally integrated neural networks during resting-state fMRI differed between adolescence and adulthood. Independent component analysis (ICA) was used to identify functionally connected neural networks in 100 healthy participants aged 12-30 years. Hemodynamic timecourses that represented integrated neural network activity were analyzed with tools that quantified system "causal density" estimates, which indexed the proportion of significant Granger causality relationships among system nodes. Mutual influences among networks decreased with age, likely reflecting stronger within-network connectivity and more efficient between-network influences with greater development. Supplemental tests showed that this normative age-related reduction in causal density was accompanied by fewer significant connections to and from each network, regional increases in the strength of functional integration within networks, and age-related reductions in the strength of numerous specific system interactions. The latter included paths between lateral prefrontal-parietal circuits and "default mode" networks. These results contribute to an emerging understanding that activity in widely distributed networks thought to underlie complex cognition influences activity in other networks. (c) 2009 Wiley-Liss, Inc.
A Strategy for Identifying Quantitative Trait Genes Using Gene Expression Analysis and Causal Analysis.

PubMed

Ishikawa, Akira

2017-11-27

Large numbers of quantitative trait loci (QTL) affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identifying causal quantitative trait genes (QTGs) for common QTL with relatively small phenotypic effects. A traditional strategy based on techniques such as positional cloning does not always enable identification of a single candidate gene for a QTL of interest because it is difficult to narrow down a target genomic interval of the QTL to a very small interval harboring only one gene. A combination of gene expression analysis and statistical causal analysis can greatly reduce the number of candidate genes. This integrated approach provides causal evidence that one of the candidate genes is a putative QTG for the QTL. Using this approach, I have recently succeeded in identifying a single putative QTG for resistance to obesity in mice. Here, I outline the integration approach and discuss its usefulness using my studies as an example.
Predictions of the causal entropic principle for environmental conditions of the universe

NASA Astrophysics Data System (ADS)

Cline, James M.; Frey, Andrew R.; Holder, Gilbert

2008-03-01

The causal entropic principle has been proposed as an alternative to the anthropic principle for understanding the magnitude of the cosmological constant. In this approach, the probability to create observers is assumed to be proportional to the entropy production ΔS in a maximal causally connected region—the causal diamond. We improve on the original treatment by better quantifying the entropy production due to stars, using an analytic model for the star formation history which accurately accounts for changes in cosmological parameters. We calculate the dependence of ΔS on the density contrast Q=δρ/ρ, and find that our universe is much closer to the most probable value of Q than in the usual anthropic approach and that probabilities are relatively weakly dependent on this amplitude. In addition, we make first estimates of the dependence of ΔS on the baryon fraction and overall matter abundance. Finally, we also explore the possibility that decays of dark matter, suggested by various observed gamma ray excesses, might produce a comparable amount of entropy to stars.
Leverage effect and its causality in the Korea composite stock price index

NASA Astrophysics Data System (ADS)

Lee, Chang-Yong

2012-02-01

In this paper, we investigate the leverage effect and its causality in the time series of the Korea Composite Stock Price Index from November of 1997 to September of 2010. The leverage effect, which can be quantitatively expressed as a negative correlation between past return and future volatility, is measured by using the cross-correlation coefficient of different time lags between the two time series of the return and the volatility. We find that past return and future volatility are negatively correlated and that the cross correlation is moderate and decays over 60 trading days. We also carry out a partial correlation analysis in order to confirm that the negative correlation between past return and future volatility is neither an artifact nor influenced by the traded volume. To determine the causality of the leverage effect within the decay time, we additionally estimate the cross correlation between past volatility and future return. With the estimate, we perform a statistical hypothesis test to demonstrate that the causal relation is in favor of the return influencing the volatility rather than the other way around.
A Note on the Usefulness of the Behavioural Rasch Selection Model for Causal Inference in the Social Sciences

NASA Astrophysics Data System (ADS)

Rabbitt, Matthew P.

2016-11-01

Social scientists are often interested in examining causal relationships where the outcome of interest is represented by an intangible concept, such as an individual's well-being or ability. Estimating causal relationships in this scenario is particularly challenging because the social scientist must rely on measurement models to measure individual's properties or attributes and then address issues related to survey data, such as omitted variables. In this paper, the usefulness of the recently proposed behavioural Rasch selection model is explored using a series of Monte Carlo experiments. The behavioural Rasch selection model is particularly useful for these types of applications because it is capable of estimating the causal effect of a binary treatment effect on an outcome that is represented by an intangible concept using cross-sectional data. Other methodology typically relies of summary measures from measurement models that require additional assumptions, some of which make these approaches less efficient. Recommendations for application of the behavioural Rasch selection model are made based on results from the Monte Carlo experiments.
Synchrony dynamics underlying effective connectivity reconstruction of neuronal circuits

NASA Astrophysics Data System (ADS)

Yu, Haitao; Guo, Xinmeng; Qin, Qing; Deng, Yun; Wang, Jiang; Liu, Jing; Cao, Yibin

2017-04-01

Reconstruction of effective connectivity between neurons is essential for neural systems with function-related significance, characterizing directionally causal influences among neurons. In this work, causal interactions between neurons in spinal dorsal root ganglion, activated by manual acupuncture at Zusanli acupoint of experimental rats, are estimated using Granger causality (GC) method. Different patterns of effective connectivity are obtained for different frequencies and types of acupuncture. Combined with synchrony analysis between neurons, we show a dependence of effective connection on the synchronization dynamics. Based on the experimental findings, a neuronal circuit model with synaptic connections is constructed. The variation of neuronal effective connectivity with respect to its structural connectivity and synchronization dynamics is further explored. Simulation results show that reciprocally causal interactions with statistically significant are formed between well-synchronized neurons. The effective connectivity may be not necessarily equivalent to synaptic connections, but rather depend on the synchrony relationship. Furthermore, transitions of effective interaction between neurons are observed following the synchronization transitions induced by conduction delay and synaptic conductance. These findings are helpful to further investigate the dynamical mechanisms underlying the reconstruction of effective connectivity of neuronal population.
Causal mediation analysis with multiple causally non-ordered mediators.

PubMed

Taguri, Masataka; Featherstone, John; Cheng, Jing

2018-01-01

In many health studies, researchers are interested in estimating the treatment effects on the outcome around and through an intermediate variable. Such causal mediation analyses aim to understand the mechanisms that explain the treatment effect. Although multiple mediators are often involved in real studies, most of the literature considered mediation analyses with one mediator at a time. In this article, we consider mediation analyses when there are causally non-ordered multiple mediators. Even if the mediators do not affect each other, the sum of two indirect effects through the two mediators considered separately may diverge from the joint natural indirect effect when there are additive interactions between the effects of the two mediators on the outcome. Therefore, we derive an equation for the joint natural indirect effect based on the individual mediation effects and their interactive effect, which helps us understand how the mediation effect works through the two mediators and relative contributions of the mediators and their interaction. We also discuss an extension for three mediators. The proposed method is illustrated using data from a randomized trial on the prevention of dental caries.
Prediction versus aetiology: common pitfalls and how to avoid them.

PubMed

van Diepen, Merel; Ramspek, Chava L; Jager, Kitty J; Zoccali, Carmine; Dekker, Friedo W

2017-04-01

Prediction research is a distinct field of epidemiologic research, which should be clearly separated from aetiological research. Both prediction and aetiology make use of multivariable modelling, but the underlying research aim and interpretation of results are very different. Aetiology aims at uncovering the causal effect of a specific risk factor on an outcome, adjusting for confounding factors that are selected based on pre-existing knowledge of causal relations. In contrast, prediction aims at accurately predicting the risk of an outcome using multiple predictors collectively, where the final prediction model is usually based on statistically significant, but not necessarily causal, associations in the data at hand.In both scientific and clinical practice, however, the two are often confused, resulting in poor-quality publications with limited interpretability and applicability. A major problem is the frequently encountered aetiological interpretation of prediction results, where individual variables in a prediction model are attributed causal meaning. This article stresses the differences in use and interpretation of aetiological and prediction studies, and gives examples of common pitfalls. © The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.
Depression and Genetic Causal Attribution of Epilepsy in Multiplex Epilepsy Families

PubMed Central

Sorge, Shawn T.; Hesdorffer, Dale C.; Phelan, Jo C.; Winawer, Melodie R.; Shostak, Sara; Goldsmith, Jeff; Chung, Wendy K.; Ottman, Ruth

2016-01-01

Summary Objectives Rapid advances in genetic research and increased use of genetic testing have increased the emphasis on genetic causes of epilepsy in patient encounters. Research in other disorders suggests that genetic causal attributions can influence patients’ psychological responses and coping strategies, but little is currently known about how epilepsy patients and their relatives will respond to genetic attributions of epilepsy. We investigated the possibility that depression, the most frequent psychiatric comorbidity in the epilepsies, might be related to the perception that epilepsy has a genetic cause among members of families containing multiple individuals with epilepsy. Methods A self-administered survey was completed by 417 individuals in 104 families averaging four individuals with epilepsy per family. Current depression was measured with the PHQ-9. Genetic causal attribution was assessed by three questions addressing: perceived likelihood of having an epilepsy-related mutation, perceived role of genetics in causing epilepsy in the family, and (in individuals with epilepsy) perceived influence of genetics in causing the individual’s epilepsy. Relatives without epilepsy were asked about their perceived chance of developing epilepsy in the future, compared with the average person. Results Prevalence of current depression was 14.8% in 182 individuals with epilepsy, 6.5% in 184 biological relatives without epilepsy, and 3.9% in 51 married-in individuals. Among individuals with epilepsy, depression was unrelated to genetic attribution. Among biological relatives without epilepsy, however, prevalence of depression increased with increasing perceived chance of having an epilepsy-related mutation (p=0.02). This association was not mediated by perceived future epilepsy risk among relatives without epilepsy. Significance Depression is associated with perceived likelihood of carrying an epilepsy-related mutation among individuals without epilepsy in families containing multiple affected individuals. This association should be considered when addressing mental health issues in such families. PMID:27558297
Kramers-Kronig relations and causality conditions for graphene in the framework of the Dirac model

NASA Astrophysics Data System (ADS)

Klimchitskaya, G. L.; Mostepanenko, V. M.

2018-04-01

We analyze the concept of causality for the conductivity of graphene described by the Dirac model. It is recalled that the condition of causality leads to the analyticity of conductivity in the upper half-plane of complex frequencies and to the standard symmetry properties for its real and imaginary parts. This results in the Kramers-Kronig relations, which explicit form depends on whether the conductivity has no pole at zero frequency (as in the case of zero temperature when the band gap of graphene is larger than twice the chemical potential) or it has a pole (as in all other cases, specifically, at nonzero temperature). Through the direct analytic calculation it is shown that the real and imaginary parts of graphene conductivity, found recently on the basis of first principles of thermal quantum field theory using the polarization tensor in (2 +1 )-dimensional space-time, satisfy the Kramers-Kronig relations precisely. In so doing, the values of two integrals in the commonly used tables, which are also important for a wider area of dispersion relations in quantum field theory and elementary particle physics, are corrected. The obtained results are not of only fundamental theoretical character, but can be used as a guideline in testing the validity of different phenomenological approaches and for the interpretation of experimental data.
Multiple mechanisms influencing the relationship between alcohol consumption and peer alcohol use.

PubMed

Edwards, Alexis C; Maes, Hermine H; Prescott, Carol A; Kendler, Kenneth S

2015-02-01

Alcohol consumption is typically correlated with the alcohol use behaviors of one's peers. Previous research has suggested that this positive relationship could be due to social selection, social influence, or a combination of both processes. However, few studies have considered the role of shared genetic and environmental influences in conjunction with causal processes. This study uses data from a sample of male twins (N = 1,790) who provided retrospective reports of their own alcohol consumption and their peers' alcohol-related behaviors, from adolescence into young adulthood (ages 12 to 25). Structural equation modeling was employed to compare 3 plausible models of genetic and environmental influences on the relationship between phenotypes over time. Model fitting indicated that one's own alcohol consumption and the alcohol use of one's peers are related through both genetic and shared environmental factors and through unique environmental causal influences. The relative magnitude of these factors, and their contribution to covariation, changed over time, with genetic factors becoming more meaningful later in development. Peers' alcohol use behaviors and one's own alcohol consumption are related through a complex combination of genetic and environmental factors that act via correlated factors and the complementary causal mechanisms of social selection and influence. Understanding these processes can inform risk assessment as well as improve our ability to model the development of alcohol use. Copyright © 2015 by the Research Society on Alcoholism.
Multiple mechanisms influencing the relationship between alcohol consumption and peer alcohol use

PubMed Central

Edwards, Alexis C.; Maesr, Hermine H.; Prescott, Carol A.; Kendler, Kenneth S.

2014-01-01

Background Alcohol consumption is typically correlated with the alcohol use behaviors of one’s peers. Previous research has suggested that this positive relationship could be due to social selection, social influence, or a combination of both processes. However, few studies have considered the role of shared genetic and environmental influences in conjunction with causal processes. Methods The current study uses data from a sample of male twins (N=1790) who provided retrospective reports of their own alcohol consumption and their peers’ alcohol related behaviors, from adolescence into young adulthood (ages 12–25). Structural equation modeling was employed to compare three plausible models of genetic and environmental influences on the relationship between phenotypes over time. Results Model fitting indicated that one’s own alcohol consumption and the alcohol use of one’s peers are related through both genetic and shared environmental factors and through unique environmental causal influences. The relative magnitude of these factors, and their contribution to covariation, changed over time, with genetic factors becoming more meaningful later in development. Conclusions Peers’ alcohol use behaviors and one’s own alcohol consumption are related through a complex combination of genetic and environmental factors that act via correlated factors and the complementary causal mechanisms of social selection and influence. Understanding these processes can inform risk assessment as well as improve our ability to model the development of alcohol use. PMID:25597346
Calculating and Understanding: Formal Models and Causal Explanations in Science, Common Reasoning and Physics Teaching

NASA Astrophysics Data System (ADS)

Besson, Ugo

2010-03-01

This paper presents an analysis of the different types of reasoning and physical explanation used in science, common thought, and physics teaching. It then reflects on the learning difficulties connected with these various approaches, and suggests some possible didactic strategies. Although causal reasoning occurs very frequently in common thought and daily life, it has long been the subject of debate and criticism among philosophers and scientists. In this paper, I begin by providing a description of some general tendencies of common reasoning that have been identified by didactic research. Thereafter, I briefly discuss the role of causality in science, as well as some different types of explanation employed in the field of physics. I then present some results of a study examining the causal reasoning used by students in solid and fluid mechanics. The differences found between the types of reasoning typical of common thought and those usually proposed during instruction can create learning difficulties and impede student motivation. Many students do not seem satisfied by the mere application of formal laws and functional relations. Instead, they express the need for a causal explanation, a mechanism that allows them to understand how a state of affairs has come about. I discuss few didactic strategies aimed at overcoming these problems, and describe, in general terms, two examples of mechanics teaching sequences which were developed and tested in different contexts. The paper ends with a reflection on the possible role to be played in physics learning by intuitive and imaginative thought, and the use of simple explanatory models based on physical analogies and causal mechanisms.
Causal beliefs of the public and social acceptance of persons with mental illness: a comparative analysis of schizophrenia, depression and alcohol dependence.

PubMed

Schomerus, G; Matschinger, H; Angermeyer, M C

2014-01-01

There is an ongoing debate whether biological illness explanations improve tolerance towards persons with mental illness or not. Several theoretical models have been proposed to predict the relationship between causal beliefs and social acceptance. This study uses path models to compare different theoretical predictions regarding attitudes towards persons with schizophrenia, depression and alcohol dependence. In a representative population survey in Germany (n = 3642), we elicited agreement with belief in biogenetic causes, current stress and childhood adversities as causes of either disorder as described in an unlabelled case vignette. We further elicited potentially mediating attitudes related to different theories about the consequences of biogenetic causal beliefs (attribution theory: onset responsibility, offset responsibility; genetic essentialism: differentness, dangerousness; genetic optimism: treatability) and social acceptance. For each vignette condition, we calculated a multiple mediator path model containing all variables. Biogenetic beliefs were associated with lower social acceptance in schizophrenia and depression, and with higher acceptance in alcohol dependence. In schizophrenia and depression, perceived differentness and dangerousness mediated the largest indirect effects, the consequences of biogenetic causal explanations thus being in accordance with the predictions of genetic essentialism. Psychosocial causal beliefs had differential effects: belief in current stress as a cause was associated with higher acceptance in schizophrenia, while belief in childhood adversities resulted in lower acceptance of a person with depression. Biological causal explanations seem beneficial in alcohol dependence, but harmful in schizophrenia and depression. The negative correlates of believing in childhood adversities as a cause of depression merit further exploration.
Causality Assessment of Serious Neurologic Adverse Events Following 2009 H1N1 Vaccination

PubMed Central

Williams, S Elizabeth; Pahud, Barbara A; Vellozzi, Claudia; Donofrio, Peter D; Dekker, Cornelia L; Halsey, Neal; Klein, Nicola P; Baxter, Roger P; Marchant, Colin D; LaRussa, Philip S; Barnett, Elizabeth D; Tokars, Jerome I; McGeeney, Brian E; Sparks, Robert C; Aukes, Laurie L.; Jakob, Kathleen; Coronel, Silvia; Sejvar, James J; Slade, Barbara A; Edwards, Kathryn M

2016-01-01

Background Adverse events occurring after vaccination are routinely reported to the Vaccine Adverse Event Reporting System (VAERS). We studied serious adverse events (SAEs) of a neurologic nature reported after receipt of influenza A (H1N1) 2009 monovalent vaccine during the 2009–10 influenza season. Investigators in the Clinical Immunization Safety Assessment (CISA) Network sought to characterize these SAEs and to assess their possible causal relationship to vaccination. Methods Centers for Disease Control and Prevention (CDC) and Food and Drug Administration (FDA) physicians reviewed all SAE reports (as defined by the Code of Federal Regulations, 21CFR§314.80) after receipt of H1N1 vaccine reported to VAERS between October 1st 2009 and March 31st 2010. Non-fatal SAE reports with neurologic presentation were referred to CISA investigators, who requested and reviewed additional medical records and clinical information as available. CISA investigators assessed the causal relationship between vaccination and the event using modified WHO criteria as defined. Results 212 VAERS reports of non-fatal serious neurological events were referred for CISA review. Case reports were equally distributed by gender (50.9% female) with an age range of 6 months to 83 years (median 38 years). The most frequent diagnoses reviewed were: Guillain-Barré Syndrome (37.3%), seizures (10.8%), cranial neuropathy (5.7%), and acute disseminated encephalomyelitis (3.8%). Causality assessment resulted in classification of 72 events as “possibly” related (33%), 108 as “unlikely” related (51%), and 20 as “unrelated” (9%) to H1N1 vaccination; none were classified as “probable” or “definite” and 12 were unclassifiable (6%). Conclusion The absence of a specific test to indicate whether a vaccine component contributes to the pathogenesis of an event occurring within a biologically plausible time period makes assessing causality difficult. The development of standardized protocols for providers to use in evaluation of adverse events following immunization, and rapid identification and follow-up of VAERS reports could improve causality assessment. PMID:21893148
Bidirectional Causal Connectivity in the Cortico-Limbic-Cerebellar Circuit Related to Structural Alterations in First-Episode, Drug-Naive Somatization Disorder

PubMed Central

Li, Ranran; Liu, Feng; Su, Qinji; Zhang, Zhikun; Zhao, Jin; Wang, Ying; Wu, Renrong; Zhao, Jingping; Guo, Wenbin

2018-01-01

Background: Anatomical and functional deficits in the cortico-limbic-cerebellar circuit are involved in the neurobiology of somatization disorder (SD). The present study was performed to examine causal connectivity of the cortico-limbic-cerebellar circuit related to structural deficits in first-episode, drug-naive patients with SD at rest. Methods: A total of 25 first-episode, drug-naive patients with SD and 28 healthy controls underwent structural and resting-state functional magnetic resonance imaging. Voxel-based morphometry and Granger causality analysis (GCA) were used to analyze the data. Results: Results showed that patients with SD exhibited decreased gray matter volume (GMV) in the right cerebellum Crus I, and increased GMV in the left anterior cingulate cortex (ACC), right middle frontal gyrus (MFG), and left angular gyrus. Causal connectivity of the cortico-limbic-cerebellar circuit was partly affected by structural alterations in the patients. Patients with SD showed bidirectional cortico-limbic connectivity abnormalities and bidirectional cortico-cerebellar and limbic-cerebellar connectivity abnormalities. The mean GMV of the right MFG was negatively correlated with the scores of the somatization subscale of the symptom checklist-90 and persistent error response of the Wisconsin Card Sorting Test (WCST) in the patients. A negative correlation was observed between increased driving connectivity from the right MFG to the right fusiform gyrus/cerebellum IV, V and the scores of the Eysenck Personality Questionnaire extraversion subscale. The mean GMV of the left ACC was negatively correlated with the WCST number of errors and persistent error response. Negative correlation was found between the causal effect from the left ACC to the right middle temporal gyrus and the scores of WCST number of categories achieved. Conclusions: Our findings show the partial effects of structural alterations on the cortico-limbic-cerebellar circuit in first-episode, drug-naive patients with SD. Correlations are observed between anatomical alterations or causal effects and clinical variables in patients with SD, and bear clinical significance. The present study emphasizes the importance of the cortico-limbic-cerebellar circuit in the neurobiology of SD. PMID:29755373
Constructions relatives et articulations discursives (Relative Constructions and Discursive Articulations)

ERIC Educational Resources Information Center

Henry, Paul

1975-01-01

Contrasts classical grammar, which concerned itself with the causal relationships of thought, universal order, and language, with modern linguistics, which tends to entirely absorb the matter of discourse. (Text is in French.) (Author/MSE)
What did Kramers and Kronig do and how did they do it?

NASA Astrophysics Data System (ADS)

Bohren, Craig F.

2010-05-01

Over time the account of how the Kramers-Kronig (dispersion) relations between the real and imaginary parts of response functions were derived in 1926 and 1927 has been transmogrified into anecdotes about what might have been done but was not. Although Kramers obtained both members of a pair of relations, Kronig obtained only one. Both authors appealed to specific models of an atomic gas rather than to the general arguments about linearity, causality and analyticity in modern model-independent derivations. Kramers merely speculated on whether the specific results he obtained might have a more general validity. Neither author showed that a signal cannot travel faster than cin any medium for which the dispersion relations are satisfied. Indeed, they did not mention, even obliquely, signal speeds and causality. Despite their magical aura, Kramers-Kronig relations are translations into somewhat cryptic frequency language of statements clearer in time language.

Optimal causal inference: estimating stored information and approximating causal architecture.

PubMed

Still, Susanne; Crutchfield, James P; Ellison, Christopher J

2010-09-01

We introduce an approach to inferring the causal architecture of stochastic dynamical systems that extends rate-distortion theory to use causal shielding--a natural principle of learning. We study two distinct cases of causal inference: optimal causal filtering and optimal causal estimation. Filtering corresponds to the ideal case in which the probability distribution of measurement sequences is known, giving a principled method to approximate a system's causal structure at a desired level of representation. We show that in the limit in which a model-complexity constraint is relaxed, filtering finds the exact causal architecture of a stochastic dynamical system, known as the causal-state partition. From this, one can estimate the amount of historical information the process stores. More generally, causal filtering finds a graded model-complexity hierarchy of approximations to the causal architecture. Abrupt changes in the hierarchy, as a function of approximation, capture distinct scales of structural organization. For nonideal cases with finite data, we show how the correct number of the underlying causal states can be found by optimal causal estimation. A previously derived model-complexity control term allows us to correct for the effect of statistical fluctuations in probability estimates and thereby avoid overfitting.
Causal Conceptions in Social Explanation and Moral Evaluation: A Historical Tour.

PubMed

Alicke, Mark D; Mandel, David R; Hilton, Denis J; Gerstenberg, Tobias; Lagnado, David A

2015-11-01

Understanding the causes of human behavior is essential for advancing one's interests and for coordinating social relations. The scientific study of how people arrive at such understandings or explanations has unfolded in four distinguishable epochs in psychology, each characterized by a different metaphor that researchers have used to represent how people think as they attribute causality and blame to other individuals. The first epoch was guided by an "intuitive scientist" metaphor, which emphasized whether observers perceived behavior to be caused by the unique tendencies of the actor or by common reactions to the requirements of the situation. This metaphor was displaced in the second epoch by an "intuitive lawyer" depiction that focused on the need to hold people responsible for their misdeeds. The third epoch was dominated by theories of counterfactual thinking, which conveyed a "person as reconstructor" approach that emphasized the antecedents and consequences of imagining alternatives to events, especially harmful ones. With the current upsurge in moral psychology, the fourth epoch emphasizes the moral-evaluative aspect of causal judgment, reflected in a "person as moralist" metaphor. By tracing the progression from the person-environment distinction in early attribution theories to present concerns with moral judgment, our goal is to clarify how causal constructs have been used, how they relate to one another, and what unique attributional problems each addresses. © Her Majesty the Queen in Right of Canada, as represented by Defence Research and Development Canada 2015.
[Hepatotoxicity associated with the use of Herbalife].

PubMed

Jóhannsson, Magnús; Ormarsdóttir, Sif; Olafsson, Sigurdur

2010-03-01

Many herbal products are known to be hepatotoxic. In a recent survey in Iceland concerning adverse reactions related to herbal medicines, Herbalife products were implicated in the majority of the reported cases of hepatotoxicity. The clinical presentations of five cases of Herbalife related liver injury during the period of 1999-2008 are analysed. Causality was assessed by using the WHO-UMC system for causality assessment and the RUCAM method. Of the five cases there were four females and one male; median age was 46 years (range 29-78). Herbalife had been used for 1 to 7 months prior to presentation. Four patients presented with a hepatocellular and one with a cholestatic reaction. Median values were for bilirubin 190 micromol/L (range: 26-311; ref. < 20 micromol/L), ALP 407 U/L (range: 149-712; ref. 35-105 U/L) and ALT 24 87 U/L (range: 456-2637; ref. 70 and 45 U/L for males and females, respectively). Liver biopsy was performed in 2 patients and was consistent with toxic hepatitis in both cases. Other causes of hepatitis were excluded by appropriate serological testing and ultrasound. Causality assessment according to RUCAM was probable in three cases and possible in two. Using the WHO-UMC criteria causality was certain in one case, probable in two and possible in two cases. Hepatotoxicity is probably associated with the use of Herbalife products. Hepatotoxicity due to herbal remedies is an important differential diagnosis in the diagnostic work-up of liver injury.
ON THE RELATION OF BACTERIA TO SO-CALLED "CHEMICAL PNEUMONIA"

PubMed Central

Koontz, A. R.; Allen, M. S.

1929-01-01

The question of a causal relation of the bacteria in gassed lungs to the pneumonia present cannot be regarded as decided. It may be said that: 1. The appearance of gassed lungs with pneumonia is very similar to the pneumonia of known bacterial origin. 2. In a few cases the type of pneumonia found coincides with the reported cases of so-called "chemical pneumonia," which is characterized by a preponderance of epithelial cells in the exudate. 3. Gassed lungs are not sterile but show highly varying numbers of bacteria. 4. The bacteria are not intracellular and are not present in large numbers in the majority of cases. The arguments for and against a causal relationship between the bacteria and the pneumonia may be summed up as follows: 1. Against a Causal Relationship a. The early appearance of pneumonia after gassing. b. The occurrence of pneumonia with very small numbers of bacteria present. c. The fact that very few bacteria are engulfed by leucocytes in gassed lungs, whereas large numbers are present in the non-gassed pneumonias and are conspicuously intracellular. 2. In Favor of a Causal Relationship a. The presence of bacteria in any numbers. b. The picture of broncho-pneumonia presented is similar to broncho-pneumonia of known bacterial origin. c. Pneumonias characterized by large numbers of epithelial cells in the exudate (so-called "chemical pneumonia") occur in animals that were never gassed or subjected to other irritating substances in any way. PMID:19869606
Evaluating the impact of implementation factors on family-based prevention programming: methods for strengthening causal inference.

PubMed

Crowley, D Max; Coffman, Donna L; Feinberg, Mark E; Greenberg, Mark T; Spoth, Richard L

2014-04-01

Despite growing recognition of the important role implementation plays in successful prevention efforts, relatively little work has sought to demonstrate a causal relationship between implementation factors and participant outcomes. In turn, failure to explore the implementation-to-outcome link limits our understanding of the mechanisms essential to successful programming. This gap is partially due to the inability of current methodological procedures within prevention science to account for the multitude of confounders responsible for variation in implementation factors (i.e., selection bias). The current paper illustrates how propensity and marginal structural models can be used to improve causal inferences involving implementation factors not easily randomized (e.g., participant attendance). We first present analytic steps for simultaneously evaluating the impact of multiple implementation factors on prevention program outcome. Then, we demonstrate this approach for evaluating the impact of enrollment and attendance in a family program, over and above the impact of a school-based program, within PROSPER, a large-scale real-world prevention trial. Findings illustrate the capacity of this approach to successfully account for confounders that influence enrollment and attendance, thereby more accurately representing true causal relations. For instance, after accounting for selection bias, we observed a 5% reduction in the prevalence of 11th grade underage drinking for those who chose to receive a family program and school program compared to those who received only the school program. Further, we detected a 7% reduction in underage drinking for those with high attendance in the family program.
Testing the Causal Direction of Mediation Effects in Randomized Intervention Studies.

PubMed

Wiedermann, Wolfgang; Li, Xintong; von Eye, Alexander

2018-05-21

In a recent update of the standards for evidence in research on prevention interventions, the Society of Prevention Research emphasizes the importance of evaluating and testing the causal mechanism through which an intervention is expected to have an effect on an outcome. Mediation analysis is commonly applied to study such causal processes. However, these analytic tools are limited in their potential to fully understand the role of theorized mediators. For example, in a design where the treatment x is randomized and the mediator (m) and the outcome (y) are measured cross-sectionally, the causal direction of the hypothesized mediator-outcome relation is not uniquely identified. That is, both mediation models, x → m → y or x → y → m, may be plausible candidates to describe the underlying intervention theory. As a third explanation, unobserved confounders can still be responsible for the mediator-outcome association. The present study introduces principles of direction dependence which can be used to empirically evaluate these competing explanatory theories. We show that, under certain conditions, third higher moments of variables (i.e., skewness and co-skewness) can be used to uniquely identify the direction of a mediator-outcome relation. Significance procedures compatible with direction dependence are introduced and results of a simulation study are reported that demonstrate the performance of the tests. An empirical example is given for illustrative purposes and a software implementation of the proposed method is provided in SPSS.
The temporal stability and predictive validity of pupils' causal attributions for difficult classroom behaviour.

PubMed

Lambert, Nathan; Miller, Andy

2010-12-01

Recent studies have investigated the causal attributions for difficult pupil behaviour made by teachers, pupils, and parents but none have investigated the temporal stability or predictive validity of these attributions. This study examines the causal attributions made for difficult classroom behaviour by students on two occasions 30 months apart. The longitudinal stability of these attributions is considered as is the predictive validity of the first set of attributions in relation to teachers' later judgments about individual students' behaviour. Two hundred and seventeen secondary school age pupils (114 males, 103 females) provided data on the two occasions. Teachers also rated each student's behaviour at the two times. A questionnaire listing 63 possible causes of classroom misbehaviour was delivered to pupils firstly when they were in Year 7 (aged 11-12) and then again, 30 months later. Responses were analysed through exploratory factor analysis (EFA). Additionally, teachers were asked to rate the standard of behaviour of each of the students on the two occasions. EFA of the Years 7 and 10 data indicated that pupils' attributions yielded broadly similar five-factor models with the perceived relative importance of these factors remaining the same. Analysis also revealed a predictive relationship between pupils' attributions regarding the factor named culture of misbehaviour in Year 7, and teachers' judgments of their standard of behaviour in Year 10. The present study suggests that young adolescents' causal attributions for difficult classroom behaviour remain stable over time and are predictive of teachers' later judgments about their behaviour.
Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation

NASA Astrophysics Data System (ADS)

Cárdenas, Jhon; Orjuela-Cañón, Alvaro D.; Cerquera, Alexander; Ravelo, Antonio

2017-11-01

Different studies have used Transfer Entropy (TE) and Granger Causality (GC) computation to quantify interconnection between physiological systems. These methods have disadvantages in parametrization and availability in analytic formulas to evaluate the significance of the results. Other inconvenience is related with the assumptions in the distribution of the models generated from the data. In this document, the authors present a way to measure the causality that connect the Central Nervous System (CNS) and the Cardiac System (CS) in people diagnosed with obstructive sleep apnea syndrome (OSA) before and during treatment with continuous positive air pressure (CPAP). For this purpose, artificial neural networks were used to obtain models for GC computation, based on time series of normalized powers calculated from electrocardiography (EKG) and electroencephalography (EEG) signals recorded in polysomnography (PSG) studies.
Towards thermodynamics of universal horizons in Einstein-æther theory.

PubMed

Berglund, Per; Bhattacharyya, Jishnu; Mattingly, David

2013-02-15

Holography grew out of black hole thermodynamics, which relies on the causal structure and general covariance of general relativity. In Einstein-æther theory, a generally covariant theory with a dynamical timelike unit vector, every solution breaks local Lorentz invariance, thereby grossly modifying the causal structure of gravity. However, there are still absolute causal boundaries, called "universal horizons," which are not Killing horizons yet obey a first law of black hole mechanics and must have an entropy if they do not violate a generalized second law. We couple a scalar field to the timelike vector and show via the tunneling approach that the universal horizon radiates as a blackbody at a fixed temperature, even if the scalar field equations also violate local Lorentz invariance. This suggests that the class of holographic theories may be much broader than currently assumed.
Automatic Ability Attribution after Failure: A Dual Process View of Achievement Attribution

PubMed Central

Sakaki, Michiko; Murayama, Kou

2013-01-01

Causal attribution has been one of the most influential frameworks in the literature of achievement motivation, but previous studies considered achievement attribution as relatively deliberate and effortful processes. In the current study, we tested the hypothesis that people automatically attribute their achievement failure to their ability, but reduce the ability attribution in a controlled manner. To address this hypothesis, we measured participants’ causal attribution belief for their task failure either under the cognitive load (load condition) or with full attention (no-load condition). Across two studies, participants attributed task performance to their ability more in the load than in the no-load condition. The increased ability attribution under cognitive load further affected intrinsic motivation. These results indicate that cognitive resources available after feedback play crucial roles in determining causal attribution belief, as well as achievement motivations. PMID:23667576
Research designs and making causal inferences from health care studies.

PubMed

Flannelly, Kevin J; Jankowski, Katherine R B

2014-01-01

This article summarizes the major types of research designs used in healthcare research, including experimental, quasi-experimental, and observational studies. Observational studies are divided into survey studies (descriptive and correlational studies), case-studies and analytic studies, the last of which are commonly used in epidemiology: case-control, retrospective cohort, and prospective cohort studies. Similarities and differences among the research designs are described and the relative strength of evidence they provide is discussed. Emphasis is placed on five criteria for drawing causal inferences that are derived from the writings of the philosopher John Stuart Mill, especially his methods or canons. The application of the criteria to experimentation is explained. Particular attention is given to the degree to which different designs meet the five criteria for making causal inferences. Examples of specific studies that have used various designs in chaplaincy research are provided.
Indicators of causal agency in physical interactions: the role of the prior context.

PubMed

Mayrhofer, Ralf; Waldmann, Michael R

2014-09-01

The question how agent and patient roles are assigned to causal participants has largely been neglected in the psychological literature on force dynamics. Inspired by the linguistic theory of Dowty (1991), we propose that agency attributions are based on a prototype concept of human intervention. We predicted that the number of criteria a participant in a causal interaction shares with this prototype determines the strength of agency intuitions. We showed in two experiments using versions of Michotte's (1963) launching scenarios that agency intuitions were moderated by manipulations of the context prior to the launching event. Altering features, such as relative movement, sequence of visibility, and self-propelled motion, tended to increase agency attributions to the participant that is normally viewed as patient in the standard scenario. Copyright © 2014 Elsevier B.V. All rights reserved.
Automatic ability attribution after failure: a dual process view of achievement attribution.

PubMed

Sakaki, Michiko; Murayama, Kou

2013-01-01

Causal attribution has been one of the most influential frameworks in the literature of achievement motivation, but previous studies considered achievement attribution as relatively deliberate and effortful processes. In the current study, we tested the hypothesis that people automatically attribute their achievement failure to their ability, but reduce the ability attribution in a controlled manner. To address this hypothesis, we measured participants' causal attribution belief for their task failure either under the cognitive load (load condition) or with full attention (no-load condition). Across two studies, participants attributed task performance to their ability more in the load than in the no-load condition. The increased ability attribution under cognitive load further affected intrinsic motivation. These results indicate that cognitive resources available after feedback play crucial roles in determining causal attribution belief, as well as achievement motivations.
Women's caregiving and paid work: causal relationships in late midlife.

PubMed

Pavalko, E K; Artis, J E

1997-07-01

Care of an ill or disabled family member or friend is disproportionately done by women and typically is done in late midlife. Because this is-also a time in the life course when women's labor force participation peaks, many women faced with caregiving demands have to decide how to balance them with their employment. In this study we use the National Longitudinal Survey (NLS) of Mature Women to examine the causal relationship between employment and caring for an ill or disabled friend or relative over a three-year period. We find that employment does not affect whether or not women start caregiving, but that women who do start are more likely to reduce employment hours or stop work. Thus, the causal relationship between employment and caregiving in late midlife is largely unidirectional, with women reducing hours to meet caregiving demands.
Estimating the causal effects of smoking.

PubMed

Rubin, D B

An important application of statistics in recent years has been to address the causal effects of smoking. There is little doubt that there are health risks associated with smoking. However, more general issues concern the causal effects due to the alleged misconduct of the tobacco industry or due to programmes designed to curtail tobacco use. To address any such causal question, assumptions must be made. Although some of the issues are well known in the statistical and epidemiological literature, there does not appear to be a unified treatment that provides prescriptive guidance on the estimation of these causal effects with explication of the needed assumptions. A 'conduct attributable fraction' is derived, which allows for arbitrary changes in smoking and non-smoking health care expenditure related factors in a counterfactual world without the alleged misconduct, and therefore generalizes the traditional 'smoking attributable fraction'. The formulation presented here, although described for the problem of estimating excess health care expenditures due to the alleged misconduct of the tobacco industry, is more general. It can be applied to any outcome, such as mortality, morbidity, or income from excise taxes, as well as to any situation in which consequences due to alleged misconduct (for example, of two entities, such as the tobacco and the asbestos industries) or due to hypothetical programmes (for example, extra smoking reduction initiatives) are to be estimated. Copyright 2001 John Wiley & Sons, Ltd.
Behavior related pauses in simple spike activity of mouse Purkinje cells are linked to spike rate modulation

PubMed Central

Cao, Ying; Maran, Selva K.; Dhamala, Mukesh; Jaeger, Dieter; Heck, Detlef H.

2012-01-01

Purkinje cells (PCs) in the mammalian cerebellum express high frequency spontaneous activity with average spike rates between 30 and 200 Hz. Cerebellar nuclear (CN) neurons receive converging input from many PCs resulting in a continuous barrage of inhibitory inputs. It has been hypothesized that pauses in PC activity trigger increases in CN spiking activity. A prediction derived from this hypothesis is that pauses in PC simple spike activity represent relevant behavioral or sensory events. Here we asked whether pauses in the simple spike activity of PCs related to either fluid licking or respiration, play a special role in representing information about behavior. Both behaviors are widely represented in cerebellar PC simple spike activity. We recorded PC activity in the vermis and lobus simplex of head fixed mice while monitoring licking and respiratory behavior. Using cross correlation and Granger causality analysis we examined whether short ISIs had a different temporal relation to behavior than long ISIs or pauses. Behavior related simple spike pauses occurred during low-rate simple spike activity in both licking and breathing related PCs. Granger causality analysis revealed causal relationships between simple spike pauses and behavior. However, the same results were obtained from an analysis of surrogate spike trains with gamma ISI distributions constructed to match rate modulations of behavior related Purkinje cells. Our results therefore suggest that the occurrence of pauses in simple spike activity does not represent additional information about behavioral or sensory events that goes beyond the simple spike rate modulations. PMID:22723707
Causal relationships between milk quality and coagulation properties in Italian Holstein-Friesian dairy cattle.

PubMed

Tiezzi, Francesco; Valente, Bruno D; Cassandro, Martino; Maltecca, Christian

2015-05-13

Recently, selection for milk technological traits was initiated in the Italian dairy cattle industry based on direct measures of milk coagulation properties (MCP) such as rennet coagulation time (RCT) and curd firmness 30 min after rennet addition (a30) and on some traditional milk quality traits that are used as predictors, such as somatic cell score (SCS) and casein percentage (CAS). The aim of this study was to shed light on the causal relationships between traditional milk quality traits and MCP. Different structural equation models that included causal effects of SCS and CAS on RCT and a30 and of RCT on a30 were implemented in a Bayesian framework. Our results indicate a non-zero magnitude of the causal relationships between the traits studied. Causal effects of SCS and CAS on RCT and a30 were observed, which suggests that the relationship between milk coagulation ability and traditional milk quality traits depends more on phenotypic causal pathways than directly on common genetic influence. While RCT does not seem to be largely controlled by SCS and CAS, some of the variation in a30 depends on the phenotypes of these traits. However, a30 depends heavily on coagulation time. Our results also indicate that, when direct effects of SCS, CAS and RCT are considered simultaneously, most of the overall genetic variability of a30 is mediated by other traits. This study suggests that selection for RCT and a30 should not be performed on correlated traits such as SCS or CAS but on direct measures because the ability of milk to coagulate is improved through the causal effect that the former play on the latter, rather than from a common source of genetic variation. Breaking the causal link (e.g. standardizing SCS or CAS before the milk is processed into cheese) would reduce the impact of the improvement due to selective breeding. Since a30 depends heavily on RCT, the relative emphasis that is put on this trait should be reconsidered and weighted for the fact that the pure measure of a30 almost double-counts RCT.
The selective power of causality on memory errors.

PubMed

Marsh, Jessecae K; Kulkofsky, Sarah

2015-01-01

We tested the influence of causal links on the production of memory errors in a misinformation paradigm. Participants studied a set of statements about a person, which were presented as either individual statements or pairs of causally linked statements. Participants were then provided with causally plausible and causally implausible misinformation. We hypothesised that studying information connected with causal links would promote representing information in a more abstract manner. As such, we predicted that causal information would not provide an overall protection against memory errors, but rather would preferentially help in the rejection of misinformation that was causally implausible, given the learned causal links. In two experiments, we measured whether the causal linkage of information would be generally protective against all memory errors or only selectively protective against certain types of memory errors. Causal links helped participants reject implausible memory lures, but did not protect against plausible lures. Our results suggest that causal information may promote an abstract storage of information that helps prevent only specific types of memory errors.
Learning to learn causal models.

PubMed

Kemp, Charles; Goodman, Noah D; Tenenbaum, Joshua B

2010-09-01

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning. Copyright © 2010 Cognitive Science Society, Inc.
Spatial and temporal relationships of electrocorticographic alpha and gamma activity during auditory processing.

PubMed

Potes, Cristhian; Brunner, Peter; Gunduz, Aysegul; Knight, Robert T; Schalk, Gerwin

2014-08-15

Neuroimaging approaches have implicated multiple brain sites in musical perception, including the posterior part of the superior temporal gyrus and adjacent perisylvian areas. However, the detailed spatial and temporal relationship of neural signals that support auditory processing is largely unknown. In this study, we applied a novel inter-subject analysis approach to electrophysiological signals recorded from the surface of the brain (electrocorticography (ECoG)) in ten human subjects. This approach allowed us to reliably identify those ECoG features that were related to the processing of a complex auditory stimulus (i.e., continuous piece of music) and to investigate their spatial, temporal, and causal relationships. Our results identified stimulus-related modulations in the alpha (8-12 Hz) and high gamma (70-110 Hz) bands at neuroanatomical locations implicated in auditory processing. Specifically, we identified stimulus-related ECoG modulations in the alpha band in areas adjacent to primary auditory cortex, which are known to receive afferent auditory projections from the thalamus (80 of a total of 15,107 tested sites). In contrast, we identified stimulus-related ECoG modulations in the high gamma band not only in areas close to primary auditory cortex but also in other perisylvian areas known to be involved in higher-order auditory processing, and in superior premotor cortex (412/15,107 sites). Across all implicated areas, modulations in the high gamma band preceded those in the alpha band by 280 ms, and activity in the high gamma band causally predicted alpha activity, but not vice versa (Granger causality, p<1e(-8)). Additionally, detailed analyses using Granger causality identified causal relationships of high gamma activity between distinct locations in early auditory pathways within superior temporal gyrus (STG) and posterior STG, between posterior STG and inferior frontal cortex, and between STG and premotor cortex. Evidence suggests that these relationships reflect direct cortico-cortical connections rather than common driving input from subcortical structures such as the thalamus. In summary, our inter-subject analyses defined the spatial and temporal relationships between music-related brain activity in the alpha and high gamma bands. They provide experimental evidence supporting current theories about the putative mechanisms of alpha and gamma activity, i.e., reflections of thalamo-cortical interactions and local cortical neural activity, respectively, and the results are also in agreement with existing functional models of auditory processing. Copyright © 2014 Elsevier Inc. All rights reserved.

The Associations of Biculturalism to Prosocial Tendencies and Positive Self Evaluations.

PubMed

Carlo, Gustavo; Basilio, Camille D; Knight, George P

2016-11-01

Although some research exists on biculturalism and negative adjustment, few studies have examined the mechanisms that account for the positive correlates of biculturalism in U.S. Latino youth. Two competing reverse causal models were tested. Specifically, we examined how biculturalism among 574 U.S. Mexican adolescents ( n =296 girls; M = 17.84 years, SD = .46 years) was related to prosocial tendencies and positive self evaluation (i.e., self-esteem and general self-efficacy). The findings yielded supportive evidence for both reverse causal models suggesting that prosocial tendencies may mediate the relations between biculturalism and positive self evaluations, and that positive self evaluations may mediate the relations between biculturalism and prosocial tendencies. The implications of the role of biculturalism for understanding prosocial development and positive self evaluations in U.S. Mexican youth are discussed.
The Associations of Biculturalism to Prosocial Tendencies and Positive Self Evaluations

PubMed Central

Carlo, Gustavo; Basilio, Camille D.; Knight, George P.

2016-01-01

Although some research exists on biculturalism and negative adjustment, few studies have examined the mechanisms that account for the positive correlates of biculturalism in U.S. Latino youth. Two competing reverse causal models were tested. Specifically, we examined how biculturalism among 574 U.S. Mexican adolescents (n =296 girls; M = 17.84 years, SD = .46 years) was related to prosocial tendencies and positive self evaluation (i.e., self-esteem and general self-efficacy). The findings yielded supportive evidence for both reverse causal models suggesting that prosocial tendencies may mediate the relations between biculturalism and positive self evaluations, and that positive self evaluations may mediate the relations between biculturalism and prosocial tendencies. The implications of the role of biculturalism for understanding prosocial development and positive self evaluations in U.S. Mexican youth are discussed. PMID:28018755
Structure and Strength in Causal Induction

ERIC Educational Resources Information Center

Griffiths, Thomas L.; Tenenbaum, Joshua B.

2005-01-01

We present a framework for the rational analysis of elemental causal induction--learning about the existence of a relationship between a single cause and effect--based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship…
Effect of news coverage on the prevalence of drunk-driving behavior: evidence from a longitudinal study.

PubMed

Yanovitzky, Itzhak

2002-05-01

To examine the proposition that antidrunk driving messages in the news media contributed indirectly to the decline in drunk driving over the past two decades through their impact on related policy making processes. Time series regression techniques are applied to longitudinal data to examine the causal association between drivers' involvement in drunk-driving behavior, the volume of news coverage devoted to the drunk driving issue, and related policy making. Results show a significant contribution of news coverage to drunk-driving-related policy actions, which in turn are associated with a reduction in drunk driving among young and high-risk drivers. There was no evidence of a direct causal association between news coverage and change in drunk-driving behavior. News coverage of alcohol-related risky behaviors seems to provide a cost-effective way of reducing the prevalence of these practices by attracting institutional attention and prompting related environmental changes. Future interventions may benefit from actively seeking to influence news coverage of risky behaviors.
Exploratory Causal Analysis in Bivariate Time Series Data

NASA Astrophysics Data System (ADS)

McCracken, James M.

Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments and data analysis techniques are required for identifying causal information and relationships directly from observational data. This need has lead to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. In this thesis, the existing time series causality method of CCM is extended by introducing a new method called pairwise asymmetric inference (PAI). It is found that CCM may provide counter-intuitive causal inferences for simple dynamics with strong intuitive notions of causality, and the CCM causal inference can be a function of physical parameters that are seemingly unrelated to the existence of a driving relationship in the system. For example, a CCM causal inference might alternate between ''voltage drives current'' and ''current drives voltage'' as the frequency of the voltage signal is changed in a series circuit with a single resistor and inductor. PAI is introduced to address both of these limitations. Many of the current approaches in the times series causality literature are not computationally straightforward to apply, do not follow directly from assumptions of probabilistic causality, depend on assumed models for the time series generating process, or rely on embedding procedures. A new approach, called causal leaning, is introduced in this work to avoid these issues. The leaning is found to provide causal inferences that agree with intuition for both simple systems and more complicated empirical examples, including space weather data sets. The leaning may provide a clearer interpretation of the results than those from existing time series causality tools. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in times series data sets, but little research exists of how these tools compare to each other in practice. This work introduces and defines exploratory causal analysis (ECA) to address this issue along with the concept of data causality in the taxonomy of causal studies introduced in this work. The motivation is to provide a framework for exploring potential causal structures in time series data sets. ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise.
Stochastic modeling of neurobiological time series: Power, coherence, Granger causality, and separation of evoked responses from ongoing activity

NASA Astrophysics Data System (ADS)

Chen, Yonghong; Bressler, Steven L.; Knuth, Kevin H.; Truccolo, Wilson A.; Ding, Mingzhou

2006-06-01

In this article we consider the stochastic modeling of neurobiological time series from cognitive experiments. Our starting point is the variable-signal-plus-ongoing-activity model. From this model a differentially variable component analysis strategy is developed from a Bayesian perspective to estimate event-related signals on a single trial basis. After subtracting out the event-related signal from recorded single trial time series, the residual ongoing activity is treated as a piecewise stationary stochastic process and analyzed by an adaptive multivariate autoregressive modeling strategy which yields power, coherence, and Granger causality spectra. Results from applying these methods to local field potential recordings from monkeys performing cognitive tasks are presented.
Public Relations Roles and Systems Theory: Functional and Historicist Causal Models.

ERIC Educational Resources Information Center

Broom, Glen M.

The effectiveness of an organizations's adaptive behavior depends on the extent to which public relations concerns are considered in goal setting and program planning. The following five open systems propositions, based on a "functional" paradigm, address the complex relationship between public relations and organizational intelligence and do not…
Smooth information flow in temperature climate network reflects mass transport

NASA Astrophysics Data System (ADS)

Hlinka, Jaroslav; Jajcay, Nikola; Hartman, David; Paluš, Milan

2017-03-01

A directed climate network is constructed by Granger causality analysis of air temperature time series from a regular grid covering the whole Earth. Using winner-takes-all network thresholding approach, a structure of a smooth information flow is revealed, hidden to previous studies. The relevance of this observation is confirmed by comparison with the air mass transfer defined by the wind field. Their close relation illustrates that although the information transferred due to the causal influence is not a physical quantity, the information transfer is tied to the transfer of mass and energy.
Prior knowledge driven Granger causality analysis on gene regulatory network discovery

DOE PAGES

Yao, Shun; Yoo, Shinjae; Yu, Dantong

2015-08-28

Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets. The widely applied pairwise GC model (PGC) and other regularization strategies can lead to a significant number of false identifications when n>>T. In this study, we proposed a new method, viz., CGC-2SPR (CGC using two-step prior Ridge regularization) to resolve the problem by incorporating prior biological knowledge about a target gene data set. In our simulation experiments, themore » propose new methodology CGC-2SPR showed significant performance improvement in terms of accuracy over other widely used GC modeling (PGC, Ridge and Lasso) and MI-based (MRNET and ARACNE) methods. In addition, we applied CGC-2SPR to a real biological dataset, i.e., the yeast metabolic cycle, and discovered more true positive edges with CGC-2SPR than with the other existing methods. In our research, we noticed a “ 1+1>2” effect when we combined prior knowledge and gene expression data to discover regulatory networks. Based on causality networks, we made a functional prediction that the Abm1 gene (its functions previously were unknown) might be related to the yeast’s responses to different levels of glucose. In conclusion, our research improves causality modeling by combining heterogeneous knowledge, which is well aligned with the future direction in system biology. Furthermore, we proposed a method of Monte Carlo significance estimation (MCSE) to calculate the edge significances which provide statistical meanings to the discovered causality networks. All of our data and source codes will be available under the link https://bitbucket.org/dtyu/granger-causality/wiki/Home.« less
Exploring Causality between TV Viewing and Weight Change in Young and Middle-Aged Adults. The Cardiovascular Risk in Young Finns Study

PubMed Central

Helajärvi, Harri; Rosenström, Tom; Pahkala, Katja; Kähönen, Mika; Lehtimäki, Terho; Heinonen, Olli J.; Oikonen, Mervi; Tammelin, Tuija; Viikari, Jorma S. A.; Raitakari, Olli T.

2014-01-01

Background Television viewing time (TV time) is associated with increased weight and obesity, but it is unclear whether this relation is causal. Methods and Results We evaluated changes in TV time, waist circumference (waist) and body mass index (BMI) in participants of the population-based Cardiovascular Risk in Young Finns study (761 women, 626 men aged 33–50 years in 2011). Waist and BMI were measured, and TV time was self-reported in 2001, 2007, and 2011. Changes in waist and BMI between 2001 and 2011 were studied a) for the whole group, b) in groups with constantly low (≤1 h/d), moderate (1–3 h/d), or high (≥3 h/d) TV time, and c) in groups with ≥1 hour in-/decrease in daily TV time between 2001 and 2011. BMIs in 1986 were also evaluated. We explored the causal relationship of TV time with waist and BMI by classical temporality criterion and recently introduced causal-discovery algorithms (pairwise causality measures). Both methods supported the hypothesis that TV time is causative to weight gain, and no evidence was found for reverse or bidirectional causality. Constantly low TV time was associated with less pronounced increase in waist and BMI, and waist and BMI increase was lower with decreased TV time (P<0.05). The increase in waist and BMI was at least 2-fold in the high TV time group compared to the low TV time group (P<0.05). Adjustment for age, sex, BMI/waist in 2001, physical activity, energy intake, or smoking did not change the results. Conclusions In young and middle-aged adults, constantly high TV time is temporally antecedent to BMI and waist increase. PMID:25028965
Functional ability loss in sensory impaired and sensory unimpaired very old adults: analyzing causal relations with positive affect across four years.

PubMed

Wahl, Hans-Werner; Drapaniotis, Philipp M; Heyl, Vera

2014-11-01

This paper focuses on the relationship between functional ability (FA) and positive affect (PA), a major component of well-being, in sensory impaired very old adults (SI) compared with sensory unimpaired individuals (UI). Previous research mostly suggests a robust causal impact of FA on PA. However, some research, drawing from Fredrickson's broaden-and-build theory, also points to the possibility of an inverse causality between FA and PA. We examine in this paper both of these causal directions in SI as well as UI individuals across a 4year observation period. Additionally, we checked for the role of negative affect (NA). The T1-T2 sample comprised 81 out of 237 SI individuals (visually or hearing impaired) assessed at T1, with a mean age at T1 of 81.8years, and 87 UI individuals out of 150 assessed at T1, with a mean age at T1 of 81.5years. Established scales were used to assess FA, PA, and NA. Using cross-lagged panel analysis to examine the direction of causality, our findings indicate that FA has significant impact on PA in both the SI and the UI group, whereas the alternative causal pathway was not confirmed. Both cross-lagged relationships between FA and NA were non-significant. No group differences in path strengths between SI and UI were present. Our study provides evidence that FA is a key competence for successful emotional aging in vulnerable groups of very old adults such as SI as well as in UI adults in advanced old age. Copyright © 2014 Elsevier Inc. All rights reserved.
Exploring causality between TV viewing and weight change in young and middle-aged adults. The Cardiovascular Risk in Young Finns study.

PubMed

Helajärvi, Harri; Rosenström, Tom; Pahkala, Katja; Kähönen, Mika; Lehtimäki, Terho; Heinonen, Olli J; Oikonen, Mervi; Tammelin, Tuija; Viikari, Jorma S A; Raitakari, Olli T

2014-01-01

Television viewing time (TV time) is associated with increased weight and obesity, but it is unclear whether this relation is causal. We evaluated changes in TV time, waist circumference (waist) and body mass index (BMI) in participants of the population-based Cardiovascular Risk in Young Finns study (761 women, 626 men aged 33-50 years in 2011). Waist and BMI were measured, and TV time was self-reported in 2001, 2007, and 2011. Changes in waist and BMI between 2001 and 2011 were studied a) for the whole group, b) in groups with constantly low (≤ 1 h/d), moderate (1-3 h/d), or high (≥ 3 h/d) TV time, and c) in groups with ≥ 1 hour in-/decrease in daily TV time between 2001 and 2011. BMIs in 1986 were also evaluated. We explored the causal relationship of TV time with waist and BMI by classical temporality criterion and recently introduced causal-discovery algorithms (pairwise causality measures). Both methods supported the hypothesis that TV time is causative to weight gain, and no evidence was found for reverse or bidirectional causality. Constantly low TV time was associated with less pronounced increase in waist and BMI, and waist and BMI increase was lower with decreased TV time (P<0.05). The increase in waist and BMI was at least 2-fold in the high TV time group compared to the low TV time group (P<0.05). Adjustment for age, sex, BMI/waist in 2001, physical activity, energy intake, or smoking did not change the results. In young and middle-aged adults, constantly high TV time is temporally antecedent to BMI and waist increase.
In Support of Clinical Case Reports: A System of Causality Assessment

PubMed Central

Hamre, Harald J.; Kienle, Gunver S.

2013-01-01

The usefulness of clinical research depends on an assessment of causality. This assessment determines what constitutes clinical evidence. Case reports are an example of evidence that is frequently overlooked because it is believed they cannot address causal links between treatment and outcomes. This may be a mistake. Clarity on the topic of causality and its assessment will be of benefit for researchers and clinicians. This article outlines an overall system of causality and causality assessment. The system proposed involves two dimensions: horizontal and vertical; each of these dimensions consists of three different types of causality and three corresponding types of causality assessment. Included in this system are diverse forms of case causality illustrated with examples from everyday life and clinical medicine. Assessing case causality can complement conventional clinical research in an era of personalized medicine. PMID:24416665
Causality links among renewable energy consumption, CO2 emissions, and economic growth in Africa: evidence from a panel ARDL-PMG approach.

PubMed

Attiaoui, Imed; Toumi, Hassen; Ammouri, Bilel; Gargouri, Ilhem

2017-05-01

This research examines the causality (For the remainder of the paper, the notion of causality refers to Granger causality.) links among renewable energy consumption (REC), CO 2 emissions (CE), non-renewable energy consumption (NREC), and economic growth (GDP) using an autoregressive distributed lag model based on the pooled mean group estimation (ARDL-PMG) and applying Granger causality tests for a panel consisting of 22 African countries for the period between 1990 and 2011. There is unidirectional and irreversible short-run causality from CE to GDP. The causal direction between CE and REC is unobservable over the short-term. Moreover, we find unidirectional, short-run causality from REC to GDP. When testing per pair of variables, there are short-run bidirectional causalities among REC, CE, and GDP. However, if we add CE to the variables REC and NREC, the causality to GDP is observable, and causality from the pair REC and NREC to economic growth is neutral. Likewise, if we add NREC to the variables GDP and REC, there is causality. There are bidirectional long-run causalities among REC, CE, and GDP, which supports the feedback assumption. Causality from GDP to REC is not strong for the panel. If we test per pair of variables, the strong causality from GDP and CE to REC is neutral. The long-run PMG estimates show that NREC and gross domestic product increase CE, whereas REC decreases CE.
Traveling Wave Modes of a Plane Layered Anelastic Earth

DTIC Science & Technology

2016-05-20

dependent anelastic moduli. The anelastic moduli must be frequency dependent and satisfy the Kramers- Kronig relations to preserve causality. The...the complex, frequency dependent moduli satisfy the Kramers- Kronig relations. Stable, well-behaved numerical algorithms exist for solving the complex
SAN-RL: combining spreading activation networks and reinforcement learning to learn configurable behaviors

NASA Technical Reports Server (NTRS)

White, J.; Gaines, D. M.; Wilkes, M.; Kusumalnukool, K.; Thongchai, S.; Kawamura, K.

2001-01-01

This approach provides the agent with a causal structure, the spreading activation network, relating goals to the actions that can achieve those goals. This enables the agent to select actions relative to the goal priorities.
Emergent Geometry from Entropy and Causality

NASA Astrophysics Data System (ADS)

Engelhardt, Netta

In this thesis, we investigate the connections between the geometry of spacetime and aspects of quantum field theory such as entanglement entropy and causality. This work is motivated by the idea that spacetime geometry is an emergent phenomenon in quantum gravity, and that the physics responsible for this emergence is fundamental to quantum field theory. Part I of this thesis is focused on the interplay between spacetime and entropy, with a special emphasis on entropy due to entanglement. In general spacetimes, there exist locally-defined surfaces sensitive to the geometry that may act as local black hole boundaries or cosmological horizons; these surfaces, known as holographic screens, are argued to have a connection with the second law of thermodynamics. Holographic screens obey an area law, suggestive of an association with entropy; they are also distinguished surfaces from the perspective of the covariant entropy bound, a bound on the total entropy of a slice of the spacetime. This construction is shown to be quite general, and is formulated in both classical and perturbatively quantum theories of gravity. The remainder of Part I uses the Anti-de Sitter/ Conformal Field Theory (AdS/CFT) correspondence to both expand and constrain the connection between entanglement entropy and geometry. The AdS/CFT correspondence posits an equivalence between string theory in the "bulk" with AdS boundary conditions and certain quantum field theories. In the limit where the string theory is simply classical General Relativity, the Ryu-Takayanagi and more generally, the Hubeny-Rangamani-Takayanagi (HRT) formulae provide a way of relating the geometry of surfaces to entanglement entropy. A first-order bulk quantum correction to HRT was derived by Faulkner, Lewkowycz and Maldacena. This formula is generalized to include perturbative quantum corrections in the bulk at any (finite) order. Hurdles to spacetime emergence from entanglement entropy as described by HRT and its quantum generalizations are discussed, both at the classical and perturbatively quantum limits. In particular, several No Go Theorems are proven, indicative of a conclusion that supplementary approaches or information may be necessary to recover the full spacetime geometry. Part II of this thesis involves the relation between geometry and causality, the property that information cannot travel faster than light. Requiring this of any quantum field theory results in constraints on string theory setups that are dual to quantum field theories via the AdS/CFT correspondence. At the level of perturbative quantum gravity, it is shown that causality in the field theory constraints the causal structure in the bulk. At the level of nonperturbative quantum string theory, we find that constraints on causal signals restrict the possible ways in which curvature singularities can be resolved in string theory. Finally, a new program of research is proposed for the construction of bulk geometry from the divergences of correlation functions in the dual field theory. This divergence structure is linked to the causal structure of the bulk and of the field theory.
Cancer risk and the complexity of the interactions between environmental and host factors: HENVINET interactive diagrams as simple tools for exploring and understanding the scientific evidence.

PubMed

Merlo, Domenico F; Filiberti, Rosangela; Kobernus, Michael; Bartonova, Alena; Gamulin, Marija; Ferencic, Zeljko; Dusinska, Maria; Fucic, Aleksandra

2012-06-28

Development of graphical/visual presentations of cancer etiology caused by environmental stressors is a process that requires combining the complex biological interactions between xenobiotics in living and occupational environment with genes (gene-environment interaction) and genomic and non-genomic based disease specific mechanisms in living organisms. Traditionally, presentation of causal relationships includes the statistical association between exposure to one xenobiotic and the disease corrected for the effect of potential confounders. Within the FP6 project HENVINET, we aimed at considering together all known agents and mechanisms involved in development of selected cancer types. Selection of cancer types for causal diagrams was based on the corpus of available data and reported relative risk (RR). In constructing causal diagrams the complexity of the interactions between xenobiotics was considered a priority in the interpretation of cancer risk. Additionally, gene-environment interactions were incorporated such as polymorphisms in genes for repair and for phase I and II enzymes involved in metabolism of xenobiotics and their elimination. Information on possible age or gender susceptibility is also included. Diagrams are user friendly thanks to multistep access to information packages and the possibility of referring to related literature and a glossary of terms. Diagrams cover both chemical and physical agents (ionizing and non-ionizing radiation) and provide basic information on the strength of the association between type of exposure and cancer risk reported by human studies and supported by mechanistic studies. Causal diagrams developed within HENVINET project represent a valuable source of information for professionals working in the field of environmental health and epidemiology, and as educational material for students. Cancer risk results from a complex interaction of environmental exposures with inherited gene polymorphisms, genetic burden collected during development and non genomic capacity of response to environmental insults. In order to adopt effective preventive measures and the associated regulatory actions, a comprehensive investigation of cancer etiology is crucial. Variations and fluctuations of cancer incidence in human populations do not necessarily reflect environmental pollution policies or population distribution of polymorphisms of genes known to be associated with increased cancer risk. Tools which may be used in such a comprehensive research, including molecular biology applied to field studies, require a methodological shift from the reductionism that has been used until recently as a basic axiom in interpretation of data. The complexity of the interactions between cells, genes and the environment, i.e. the resonance of the living matter with the environment, can be synthesized by systems biology. Within the HENVINET project such philosophy was followed in order to develop interactive causal diagrams for the investigation of cancers with possible etiology in environmental exposure. Causal diagrams represent integrated knowledge and seed tool for their future development and development of similar diagrams for other environmentally related diseases such as asthma or sterility. In this paper development and application of causal diagrams for cancer are presented and discussed.
Disease Compass- a navigation system for disease knowledge based on ontology and linked data techniques.

PubMed

Kozaki, Kouji; Yamagata, Yuki; Mizoguchi, Riichiro; Imai, Takeshi; Ohe, Kazuhiko

2017-06-19

Medical ontologies are expected to contribute to the effective use of medical information resources that store considerable amount of data. In this study, we focused on disease ontology because the complicated mechanisms of diseases are related to concepts across various medical domains. The authors developed a River Flow Model (RFM) of diseases, which captures diseases as the causal chains of abnormal states. It represents causes of diseases, disease progression, and downstream consequences of diseases, which is compliant with the intuition of medical experts. In this paper, we discuss a fact repository for causal chains of disease based on the disease ontology. It could be a valuable knowledge base for advanced medical information systems. We developed the fact repository for causal chains of diseases based on our disease ontology and abnormality ontology. This section summarizes these two ontologies. It is developed as linked data so that information scientists can access it using SPARQL queries through an Resource Description Framework (RDF) model for causal chain of diseases. We designed the RDF model as an implementation of the RFM for the fact repository based on the ontological definitions of the RFM. 1554 diseases and 7080 abnormal states in six major clinical areas, which are extracted from the disease ontology, are published as linked data (RDF) with SPARQL endpoint (accessible API). Furthermore, the authors developed Disease Compass, a navigation system for disease knowledge. Disease Compass can browse the causal chains of a disease and obtain related information, including abnormal states, through two web services that provide general information from linked data, such as DBpedia, and 3D anatomical images. Disease Compass can provide a complete picture of disease-associated processes in such a way that fits with a clinician's understanding of diseases. Therefore, it supports user exploration of disease knowledge with access to pertinent information from a variety of sources.
Type 2 Diabetes, Diabetes Genetic Score and Risk of Decreased Renal Function and Albuminuria: A Mendelian Randomization Study

PubMed Central

Xu, Min; Bi, Yufang; Huang, Ya; Xie, Lan; Hao, Mingli; Zhao, Zhiyun; Xu, Yu; Lu, Jieli; Chen, Yuhong; Sun, Yimin; Qi, Lu; Wang, Weiqing; Ning, Guang

2016-01-01

Background Type 2 diabetes (T2D) is a risk factor for dysregulation of glomerular filtration rate (GFR) and albuminuria. However, whether the association is causal remains unestablished. Research Design and Methods We performed a Mendelian Randomization (MR) analysis in 11,502 participants aged 40 and above, from a well-defined community in Shanghai during 2011–2013, to explore the causal association between T2D and decreased estimated GFR (eGFR) and increased urinary albumin-to-creatinine ratio (uACR). We genotyped 34 established T2D common variants in East Asians, and created a T2D-genetic risk score (GRS). We defined decreased eGFR as eGFR < 90 ml/min/1.73 m2 and increased uACR as uACR ≥ 30 mg/g. We used the T2D_GRS as the instrumental variable (IV) to quantify the causal effect of T2D on decreased eGFR and increased uACR. Results Each 1-standard deviation (SD, 3.90 points) increment in T2D_GRS was associated with decreased eGFR: odds ratio (OR) = 1.18 (95% confidence interval [CI]: 1.01, 1.30). In the MR analysis, we demonstrated a causal relationship between genetically determined T2D and decreased eGFR (OR = 1.47, 95% CI: 1.15, 1.88, P = 0.0003). When grouping the genetic loci according to their relations with either insulin secretion (IS) or insulin resistance (IR), we found both IS_GRS and IR_GRS were significantly related to decreased eGFR (both P < 0.02). In addition, T2D_GRS and IS_GRS were significantly associated with Log-uACR (both P = 0.04). Conclusion Our results provide novel evidence for a causal association between T2D and decreased eGFR by using MR approach in a Chinese population. PMID:27211558

Type 2 Diabetes, Diabetes Genetic Score and Risk of Decreased Renal Function and Albuminuria: A Mendelian Randomization Study.

PubMed

Xu, Min; Bi, Yufang; Huang, Ya; Xie, Lan; Hao, Mingli; Zhao, Zhiyun; Xu, Yu; Lu, Jieli; Chen, Yuhong; Sun, Yimin; Qi, Lu; Wang, Weiqing; Ning, Guang

2016-04-01

Type 2 diabetes (T2D) is a risk factor for dysregulation of glomerular filtration rate (GFR) and albuminuria. However, whether the association is causal remains unestablished. We performed a Mendelian Randomization (MR) analysis in 11,502 participants aged 40 and above, from a well-defined community in Shanghai during 2011-2013, to explore the causal association between T2D and decreased estimated GFR (eGFR) and increased urinary albumin-to-creatinine ratio (uACR). We genotyped 34 established T2D common variants in East Asians, and created a T2D-genetic risk score (GRS). We defined decreased eGFR as eGFR<90ml/min/1.73m(2) and increased uACR as uACR≥30mg/g. We used the T2D_GRS as the instrumental variable (IV) to quantify the causal effect of T2D on decreased eGFR and increased uACR. Each 1-standard deviation (SD, 3.90 points) increment in T2D_GRS was associated with decreased eGFR: odds ratio (OR)=1.18 (95% confidence interval [CI]: 1.01, 1.30). In the MR analysis, we demonstrated a causal relationship between genetically determined T2D and decreased eGFR (OR=1.47, 95% CI: 1.15, 1.88, P=0.0003). When grouping the genetic loci according to their relations with either insulin secretion (IS) or insulin resistance (IR), we found both IS_GRS and IR_GRS were significantly related to decreased eGFR (both P<0.02). In addition, T2D_GRS and IS_GRS were significantly associated with Log-uACR (both P=0.04). Our results provide novel evidence for a causal association between T2D and decreased eGFR by using MR approach in a Chinese population. Copyright © 2016. Published by Elsevier B.V.
Graphical modeling of gene expression in monocytes suggests molecular mechanisms explaining increased atherosclerosis in smokers.

PubMed

Verdugo, Ricardo A; Zeller, Tanja; Rotival, Maxime; Wild, Philipp S; Münzel, Thomas; Lackner, Karl J; Weidmann, Henri; Ninio, Ewa; Trégouët, David-Alexandre; Cambien, François; Blankenberg, Stefan; Tiret, Laurence

2013-01-01

Smoking is a risk factor for atherosclerosis with reported widespread effects on gene expression in circulating blood cells. We hypothesized that a molecular signature mediating the relation between smoking and atherosclerosis may be found in the transcriptome of circulating monocytes. Genome-wide expression profiles and counts of atherosclerotic plaques in carotid arteries were collected in 248 smokers and 688 non-smokers from the general population. Patterns of co-expressed genes were identified by Independent Component Analysis (ICA) and network structure of the pattern-specific gene modules was inferred by the PC-algorithm. A likelihood-based causality test was implemented to select patterns that fit models containing a path "smoking→gene expression→plaques". Robustness of the causal inference was assessed by bootstrapping. At a FDR ≤0.10, 3,368 genes were associated to smoking or plaques, of which 93% were associated to smoking only. SASH1 showed the strongest association to smoking and PPARG the strongest association to plaques. Twenty-nine gene patterns were identified by ICA. Modules containing SASH1 and PPARG did not show evidence for the "smoking→gene expression→plaques" causality model. Conversely, three modules had good support for causal effects and exhibited a network topology consistent with gene expression mediating the relation between smoking and plaques. The network with the strongest support for causal effects was connected to plaques through SLC39A8, a gene with known association to HDL-cholesterol and cellular uptake of cadmium from tobacco, while smoking was directly connected to GAS6, a gene reported to have anti-inflammatory effects in atherosclerosis and to be up-regulated in the placenta of women smoking during pregnancy. Our analysis of the transcriptome of monocytes recovered genes relevant for association to smoking and atherosclerosis, and connected genes that before, were only studied in separate contexts. Inspection of correlation structure revealed candidates that would be missed by expression-phenotype association analysis alone.
Graphical Modeling of Gene Expression in Monocytes Suggests Molecular Mechanisms Explaining Increased Atherosclerosis in Smokers

PubMed Central

Verdugo, Ricardo A.; Zeller, Tanja; Rotival, Maxime; Wild, Philipp S.; Münzel, Thomas; Lackner, Karl J.; Weidmann, Henri; Ninio, Ewa; Trégouët, David-Alexandre; Cambien, François; Blankenberg, Stefan; Tiret, Laurence

2013-01-01

Smoking is a risk factor for atherosclerosis with reported widespread effects on gene expression in circulating blood cells. We hypothesized that a molecular signature mediating the relation between smoking and atherosclerosis may be found in the transcriptome of circulating monocytes. Genome-wide expression profiles and counts of atherosclerotic plaques in carotid arteries were collected in 248 smokers and 688 non-smokers from the general population. Patterns of co-expressed genes were identified by Independent Component Analysis (ICA) and network structure of the pattern-specific gene modules was inferred by the PC-algorithm. A likelihood-based causality test was implemented to select patterns that fit models containing a path “smoking→gene expression→plaques”. Robustness of the causal inference was assessed by bootstrapping. At a FDR ≤0.10, 3,368 genes were associated to smoking or plaques, of which 93% were associated to smoking only. SASH1 showed the strongest association to smoking and PPARG the strongest association to plaques. Twenty-nine gene patterns were identified by ICA. Modules containing SASH1 and PPARG did not show evidence for the “smoking→gene expression→plaques” causality model. Conversely, three modules had good support for causal effects and exhibited a network topology consistent with gene expression mediating the relation between smoking and plaques. The network with the strongest support for causal effects was connected to plaques through SLC39A8, a gene with known association to HDL-cholesterol and cellular uptake of cadmium from tobacco, while smoking was directly connected to GAS6, a gene reported to have anti-inflammatory effects in atherosclerosis and to be up-regulated in the placenta of women smoking during pregnancy. Our analysis of the transcriptome of monocytes recovered genes relevant for association to smoking and atherosclerosis, and connected genes that before, were only studied in separate contexts. Inspection of correlation structure revealed candidates that would be missed by expression-phenotype association analysis alone. PMID:23372645
A coordination class analysis of college students' judgments about animated motion

NASA Astrophysics Data System (ADS)

Thaden-Koch, Thomas Christian

The coordination class construct was invented by di5essa and Sherin to clarify what it means to learn and use scientific concepts. A coordination class is defined to consist of readout strategies, which guide observation, and the causal net, which contains knowledge necessary for making inferences from observations. A coordination class, as originally specified, reliably extracts a certain class of information from a variety of situations. The coordination class construct is relatively new. To examine its utility, transcripts of interviews with college students were analyzed in terms of the coordination class construct. In the interviews, students judged the realism of several computer animations depicting balls rolling on a pair of tracks. When shown animations with only one ball, students made judgments consistent with focusing on the ball's speed changes. Adding a second ball to each animation strongly affected judgments made by students taking introductory physics courses, but had a smaller effect on judgments made by students taking a psychology course. Reasoning was described in this analysis as the coordination of readouts about animations with causal net elements related to realistic motion. Decision-making was characterized both for individual students and for groups by the causal net elements expressed, by the types of readouts reported, and by the coordination processes involved. The coordination class construct was found useful for describing the elements and processes of student decision-making, but little evidence was found to suggest that the students studied possessed reliable coordination classes. Students' causal nets were found to include several appropriate expectations about realistic motion. Several students reached judgments that appeared contrary to their expectations and reported mutually incompatible expectations. Descriptions of students' decision-making processes often included faulty readouts, or feedback loops in which causal net elements or readouts were adjusted. Comparisons of the interviewed groups' coordination were found to echo differences and similarities in animation judgments made by larger groups of students who were not interviewed.
CADDIS Volume 1. Stressor Identification: About Causal Assessment

EPA Pesticide Factsheets

An introduction to the history of our approach to causal assessment, A chronology of causal history and philosophy, An introduction to causal history and philosophy, References for the Causal Assessment Background section of Stressor Identification
Stable Causal Relationships Are Better Causal Relationships.

PubMed

Vasilyeva, Nadya; Blanchard, Thomas; Lombrozo, Tania

2018-05-01

We report three experiments investigating whether people's judgments about causal relationships are sensitive to the robustness or stability of such relationships across a range of background circumstances. In Experiment 1, we demonstrate that people are more willing to endorse causal and explanatory claims based on stable (as opposed to unstable) relationships, even when the overall causal strength of the relationship is held constant. In Experiment 2, we show that this effect is not driven by a causal generalization's actual scope of application. In Experiment 3, we offer evidence that stable causal relationships may be seen as better guides to action. Collectively, these experiments document a previously underappreciated factor that shapes people's causal reasoning: the stability of the causal relationship. Copyright © 2018 Cognitive Science Society, Inc.
A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

PubMed

Lu, Hongjing; Rojas, Randall R; Beckers, Tom; Yuille, Alan L

2016-03-01

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. Copyright © 2015 Cognitive Science Society, Inc.
The causality analysis of climate change and large-scale human crisis

PubMed Central

Zhang, David D.; Lee, Harry F.; Wang, Cong; Li, Baosheng; Pei, Qing; Zhang, Jane; An, Yulun

2011-01-01

Recent studies have shown strong temporal correlations between past climate changes and societal crises. However, the specific causal mechanisms underlying this relation have not been addressed. We explored quantitative responses of 14 fine-grained agro-ecological, socioeconomic, and demographic variables to climate fluctuations from A.D. 1500–1800 in Europe. Results show that cooling from A.D. 1560–1660 caused successive agro-ecological, socioeconomic, and demographic catastrophes, leading to the General Crisis of the Seventeenth Century. We identified a set of causal linkages between climate change and human crisis. Using temperature data and climate-driven economic variables, we simulated the alternation of defined “golden” and “dark” ages in Europe and the Northern Hemisphere during the past millennium. Our findings indicate that climate change was the ultimate cause, and climate-driven economic downturn was the direct cause, of large-scale human crises in preindustrial Europe and the Northern Hemisphere. PMID:21969578
The causality analysis of climate change and large-scale human crisis.

PubMed

Zhang, David D; Lee, Harry F; Wang, Cong; Li, Baosheng; Pei, Qing; Zhang, Jane; An, Yulun

2011-10-18

Recent studies have shown strong temporal correlations between past climate changes and societal crises. However, the specific causal mechanisms underlying this relation have not been addressed. We explored quantitative responses of 14 fine-grained agro-ecological, socioeconomic, and demographic variables to climate fluctuations from A.D. 1500-1800 in Europe. Results show that cooling from A.D. 1560-1660 caused successive agro-ecological, socioeconomic, and demographic catastrophes, leading to the General Crisis of the Seventeenth Century. We identified a set of causal linkages between climate change and human crisis. Using temperature data and climate-driven economic variables, we simulated the alternation of defined "golden" and "dark" ages in Europe and the Northern Hemisphere during the past millennium. Our findings indicate that climate change was the ultimate cause, and climate-driven economic downturn was the direct cause, of large-scale human crises in preindustrial Europe and the Northern Hemisphere.
In defence of story-telling.

PubMed

Currie, Adrian; Sterelny, Kim

2017-04-01

We argue that narratives are central to the success of historical reconstruction. Narrative explanation involves tracing causal trajectories across time. The construction of narrative, then, often involves postulating relatively speculative causal connections between comparatively well-established events. But speculation is not always idle or harmful: it also aids in overcoming local underdetermination by forming scaffolds from which new evidence becomes relevant. Moreover, as our understanding of the past's causal milieus become richer, the constraints on narrative plausibility become increasingly strict: a narrative's admissibility does not turn on mere logical consistency with background data. Finally, narrative explanation and explanation generated by simple, formal models complement one another. Where models often achieve isolation and precision at the cost of simplification and abstraction, narratives can track complex changes in a trajectory over time at the cost of simplicity and precision. In combination both allow us to understand and explain highly complex historical sequences. Copyright © 2017 Elsevier Ltd. All rights reserved.
The Causal Ordering of Prominence and Salience in Identity Theory: An Empirical Examination

PubMed Central

Brenner, Philip S.; Serpe, Richard T.; Stryker, Sheldon

2016-01-01

Identity theory invokes two distinct but related concepts, identity salience and prominence, to explain how the organization of identities that make up the self impacts the probability that a given identity is situationally enacted. However, much extant research has failed to clearly distinguish between salience and prominence, and their empirical relationship has not been adequately investigated, impeding a solid understanding of the significance and role of each in a general theory of the self. This study examines their causal ordering using three waves of panel data from 48 universities focusing on respondents’ identities as science students. Analyses strongly support a causal ordering from prominence to salience. We provide theoretical and empirical grounds to justify this ordering while acknowledging potential variation in its strength across identities. Finally, we offer recommendations about the use of prominence and salience when measures of one or both are available or when analyses use cross-sectional data. PMID:27284212
Mendelian Randomization.

PubMed

Grover, Sandeep; Del Greco M, Fabiola; Stein, Catherine M; Ziegler, Andreas

2017-01-01

Confounding and reverse causality have prevented us from drawing meaningful clinical interpretation even in well-powered observational studies. Confounding may be attributed to our inability to randomize the exposure variable in observational studies. Mendelian randomization (MR) is one approach to overcome confounding. It utilizes one or more genetic polymorphisms as a proxy for the exposure variable of interest. Polymorphisms are randomly distributed in a population, they are static throughout an individual's lifetime, and may thus help in inferring directionality in exposure-outcome associations. Genome-wide association studies (GWAS) or meta-analyses of GWAS are characterized by large sample sizes and the availability of many single nucleotide polymorphisms (SNPs), making GWAS-based MR an attractive approach. GWAS-based MR comes with specific challenges, including multiple causality. Despite shortcomings, it still remains one of the most powerful techniques for inferring causality.With MR still an evolving concept with complex statistical challenges, the literature is relatively scarce in terms of providing working examples incorporating real datasets. In this chapter, we provide a step-by-step guide for causal inference based on the principles of MR with a real dataset using both individual and summary data from unrelated individuals. We suggest best possible practices and give recommendations based on the current literature.
Time-domain comparisons of power law attenuation in causal and noncausal time-fractional wave equations

PubMed Central

Zhao, Xiaofeng; McGough, Robert J.

2016-01-01

The attenuation of ultrasound propagating in human tissue follows a power law with respect to frequency that is modeled by several different causal and noncausal fractional partial differential equations. To demonstrate some of the similarities and differences that are observed in three related time-fractional partial differential equations, time-domain Green's functions are calculated numerically for the power law wave equation, the Szabo wave equation, and for the Caputo wave equation. These Green's functions are evaluated for water with a power law exponent of y = 2, breast with a power law exponent of y = 1.5, and liver with a power law exponent of y = 1.139. Simulation results show that the noncausal features of the numerically calculated time-domain response are only evident very close to the source and that these causal and noncausal time-domain Green's functions converge to the same result away from the source. When noncausal time-domain Green's functions are convolved with a short pulse, no evidence of noncausal behavior remains in the time-domain, which suggests that these causal and noncausal time-fractional models are equally effective for these numerical calculations. PMID:27250193
An Analysis on the Unemployment Rate in the Philippines: A Time Series Data Approach

NASA Astrophysics Data System (ADS)

Urrutia, J. D.; Tampis, R. L.; E Atienza, JB

2017-03-01

This study aims to formulate a mathematical model for forecasting and estimating unemployment rate in the Philippines. Also, factors which can predict the unemployment is to be determined among the considered variables namely Labor Force Rate, Population, Inflation Rate, Gross Domestic Product, and Gross National Income. Granger-causal relationship and integration among the dependent and independent variables are also examined using Pairwise Granger-causality test and Johansen Cointegration Test. The data used were acquired from the Philippine Statistics Authority, National Statistics Office, and Bangko Sentral ng Pilipinas. Following the Box-Jenkins method, the formulated model for forecasting the unemployment rate is SARIMA (6, 1, 5) × (0, 1, 1)4 with a coefficient of determination of 0.79. The actual values are 99 percent identical to the predicted values obtained through the model, and are 72 percent closely relative to the forecasted ones. According to the results of the regression analysis, Labor Force Rate and Population are the significant factors of unemployment rate. Among the independent variables, Population, GDP, and GNI showed to have a granger-causal relationship with unemployment. It is also found that there are at least four cointegrating relations between the dependent and independent variables.
Does Causal Action Facilitate Causal Perception in Infants Younger than 6 Months of Age?

ERIC Educational Resources Information Center

Rakison, David H.; Krogh, Lauren

2012-01-01

Previous research has established that infants are unable to perceive causality until 6 1/4 months of age. The current experiments examined whether infants' ability to engage in causal action could facilitate causal perception prior to this age. In Experiment 1, 4 1/2-month-olds were randomly assigned to engage in causal action experience via…
Causal capture effects in chimpanzees (Pan troglodytes).

PubMed

Matsuno, Toyomi; Tomonaga, Masaki

2017-01-01

Extracting a cause-and-effect structure from the physical world is an important demand for animals living in dynamically changing environments. Human perceptual and cognitive mechanisms are known to be sensitive and tuned to detect and interpret such causal structures. In contrast to rigorous investigations of human causal perception, the phylogenetic roots of this perception are not well understood. In the present study, we aimed to investigate the susceptibility of nonhuman animals to mechanical causality by testing whether chimpanzees perceived an illusion called causal capture (Scholl & Nakayama, 2002). Causal capture is a phenomenon in which a type of bistable visual motion of objects is perceived as causal collision due to a bias from a co-occurring causal event. In our experiments, we assessed the susceptibility of perception of a bistable stream/bounce motion event to a co-occurring causal event in chimpanzees. The results show that, similar to in humans, causal "bounce" percepts were significantly increased in chimpanzees with the addition of a task-irrelevant causal bounce event that was synchronously presented. These outcomes suggest that the perceptual mechanisms behind the visual interpretation of causal structures in the environment are evolutionarily shared between human and nonhuman animals. Copyright © 2016 Elsevier B.V. All rights reserved.
Training Implicit Social Anxiety Associations: An Experimental Intervention

PubMed Central

Clerkin, Elise M.; Teachman, Bethany A.

2010-01-01

The current study investigates an experimental anxiety reduction intervention among a highly socially anxious sample (N=108; n=36 per Condition; 80 women). Using a conditioning paradigm, our goal was to modify implicit social anxiety associations to directly test the premise from cognitive models that biased cognitive processing may be causally related to anxious responding. Participants were trained to preferentially process non-threatening information through repeated pairings of self-relevant stimuli and faces indicating positive social feedback. As expected, participants in this positive training condition (relative to our two control conditions) displayed less negative implicit associations following training, and were more likely to complete an impromptu speech (though they did not report less anxiety during the speech). These findings offer partial support for cognitive models and indicate that implicit associations are not only correlated with social anxiety, they may be causally related to anxiety reduction as well. PMID:20102788
Training implicit social anxiety associations: an experimental intervention.

PubMed

Clerkin, Elise M; Teachman, Bethany A

2010-04-01

The current study investigates an experimental anxiety reduction intervention among a highly socially anxious sample (N=108; n=36 per Condition; 80 women). Using a conditioning paradigm, our goal was to modify implicit social anxiety associations to directly test the premise from cognitive models that biased cognitive processing may be causally related to anxious responding. Participants were trained to preferentially process non-threatening information through repeated pairings of self-relevant stimuli and faces indicating positive social feedback. As expected, participants in this positive training condition (relative to our two control conditions) displayed less negative implicit associations following training, and were more likely to complete an impromptu speech (though they did not report less anxiety during the speech). These findings offer partial support for cognitive models and indicate that implicit associations are not only correlated with social anxiety, they may be causally related to anxiety reduction as well. (c) 2010 Elsevier Ltd. All rights reserved.
CaSPIAN: A Causal Compressive Sensing Algorithm for Discovering Directed Interactions in Gene Networks

PubMed Central

Emad, Amin; Milenkovic, Olgica

2014-01-01

We introduce a novel algorithm for inference of causal gene interactions, termed CaSPIAN (Causal Subspace Pursuit for Inference and Analysis of Networks), which is based on coupling compressive sensing and Granger causality techniques. The core of the approach is to discover sparse linear dependencies between shifted time series of gene expressions using a sequential list-version of the subspace pursuit reconstruction algorithm and to estimate the direction of gene interactions via Granger-type elimination. The method is conceptually simple and computationally efficient, and it allows for dealing with noisy measurements. Its performance as a stand-alone platform without biological side-information was tested on simulated networks, on the synthetic IRMA network in Saccharomyces cerevisiae, and on data pertaining to the human HeLa cell network and the SOS network in E. coli. The results produced by CaSPIAN are compared to the results of several related algorithms, demonstrating significant improvements in inference accuracy of documented interactions. These findings highlight the importance of Granger causality techniques for reducing the number of false-positives, as well as the influence of noise and sampling period on the accuracy of the estimates. In addition, the performance of the method was tested in conjunction with biological side information of the form of sparse “scaffold networks”, to which new edges were added using available RNA-seq or microarray data. These biological priors aid in increasing the sensitivity and precision of the algorithm in the small sample regime. PMID:24622336
Evaluating the Impact of Implementation Factors on Family-Based Prevention Programming: Methods for Strengthening Causal Inference

PubMed Central

Crowley, D. Max; Coffman, Donna L.; Feinberg, Mark; Greenberg, Mark; Spoth, Richard

2013-01-01

Despite growing recognition of the important role implementation plays in successful prevention efforts, relatively little work has sought to demonstrate a causal relationship between implementation factors and participant outcomes. In turn, failure to explore the implementation-to-outcome link limits our understanding of the mechanisms essential to successful programming. This gap is partially due to the inability of current methodological procedures within prevention science to account for the multitude of confounders responsible for variation in implementation factors (i.e., selection bias). The current paper illustrates how propensity and marginal structural models can be used to improve causal inferences involving implementation factors not easily randomized (e.g., participant attendance). We first present analytic steps for simultaneously evaluating the impact of multiple implementation factors on prevention program outcome. Then we demonstrate this approach for evaluating the impact of enrollment and attendance in a family program, over and above the impact of a school-based program, within PROSPER, a large scale real-world prevention trial. Findings illustrate the capacity of this approach to successfully account for confounders that influence enrollment and attendance, thereby more accurately representing true causal relations. For instance, after accounting for selection bias, we observed a 5% reduction in the prevalence of 11th grade underage drinking for those who chose to receive a family program and school program compared to those who received only the school program. Further, we detected a 7% reduction in underage drinking for those with high attendance in the family program. PMID:23430578

Great apes and children infer causal relations from patterns of variation and covariation.

PubMed

Völter, Christoph J; Sentís, Inés; Call, Josep

2016-10-01

We investigated whether nonhuman great apes (N=23), 2.5-year-old (N=20), and 3-year-old children (N=40) infer causal relations from patterns of variation and covariation by adapting the blicket detector paradigm for apes. We presented chimpanzees (Pan troglodytes), bonobos (Pan paniscus), orangutans (Pongo abelii), gorillas (Gorilla gorilla), and children (Homo sapiens) with a novel reward dispenser, the blicket detector. The detector was activated by inserting specific (yet randomly determined) objects, the so-called blickets. Once activated a reward was released, accompanied by lights and a short tone. Participants were shown different patterns of variation and covariation between two different objects and the activation of the detector. When subsequently choosing between one of the two objects to activate the detector on their own all species, except gorillas (who failed the training), took these patterns of correlation into account. In particular, apes and 2.5-year-old children ignored objects whose effect on the detector completely depended on the presence of another object. Follow-up experiments explored whether the apes and children were also able to re-evaluate evidence retrospectively. Only children (3-year-olds in particular) were able to make such retrospective inferences about causal structures from observing the effects of the experimenter's actions. Apes succeeded here only when they observed the effects of their own interventions. Together, this study provides evidence that apes, like young children, accurately infer causal structures from patterns of (co)variation and that they use this information to inform their own interventions. Copyright © 2016 Elsevier B.V. All rights reserved.
Do Selective Serotonin Reuptake Inhibitors (SSRIs) Cause Fractures?

PubMed

Warden, Stuart J; Fuchs, Robyn K

2016-10-01

Recent meta-analyses report a 70 % increase in fracture risk in selective serotonin reuptake inhibitor (SSRI) users compared to non-users; however, included studies were observational and limited in their ability to establish causality. Here, we use the Bradford Hill criteria to explore causality between SSRIs and fractures. We found a strong, consistent, and temporal relationship between SSRIs and fractures, which appears to follow a biological gradient. However, specificity and biological plausibility remain concerns. In terms of specificity, the majority of available data have limitations due to either confounding by indication or channeling bias. Self-controlled case series address some of these limitations and provide relatively strong observational evidence for a causal relationship between SSRIs and fracture. In doing so, they suggest that falls contribute to fractures in SSRI users. Whether there are also underlying changes in skeletal properties remains unresolved. Initial studies provide some evidence for skeletal effects of SSRIs; however, the pathways involved need to be established before biological plausibility can be accepted. As the link between SSRIs and fractures is based on observational data and not evidence from prospective trials, there is insufficient evidence to definitively determine a causal relationship and it appears premature to label SSRIs as a secondary cause of osteoporosis. SSRIs appear to contribute to fracture-inducing falls, and addressing any fall risk associated with SSRIs may be an efficient approach to reducing SSRI-related fractures. As fractures stemming from SSRI-induced falls are more likely in individuals with compromised bone health, it is worth considering bone density testing and intervention for those presenting with risk factors for osteoporosis.
Paracetamol in therapeutic dosages and acute liver injury: causality assessment in a prospective case series

PubMed Central

2011-01-01

Background Acute liver injury (ALI) induced by paracetamol overdose is a well known cause of emergency hospital admission and death. However, there is debate regarding the risk of ALI after therapeutic dosages of the drug. The aim is to describe the characteristics of patients admitted to hospital with jaundice who had previous exposure to therapeutic doses of paracetamol. An assessment of the causality role of paracetamol was performed in each case. Methods Based on the evaluation of prospectively gathered cases of ALI with detailed clinical information, thirty-two cases of ALI in non-alcoholic patients exposed to therapeutic doses of paracetamol were identified. Two authors assessed all drug exposures by using the CIOMS/RUCAM scale. Each case was classified into one of five categories based on the causality score for paracetamol. Results In four cases the role of paracetamol was judged to be unrelated, in two unlikely, and these were excluded from evaluation. In seven of the remaining 26 cases, the RUCAM score associated with paracetamol was higher than that associated with other concomitant medications. The estimated incidence of ALI related to the use of paracetamol in therapeutic dosages was 0.4 per million inhabitants older than 15 years of age and per year (99%CI, 0.2-0.8) and of 10 per million paracetamol users-year (95% CI 4.3-19.4). Conclusions Our results indicate that paracetamol in therapeutic dosages may be considered in the causality assessment in non-alcoholic patients with liver injury, even if the estimated incidence of ALI related to paracetamol appears to be low. PMID:21762481
Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster

PubMed Central

Edwards, Stefan M.; Sørensen, Izel F.; Sarup, Pernille; Mackay, Trudy F. C.; Sørensen, Peter

2016-01-01

Predicting individual quantitative trait phenotypes from high-resolution genomic polymorphism data is important for personalized medicine in humans, plant and animal breeding, and adaptive evolution. However, this is difficult for populations of unrelated individuals when the number of causal variants is low relative to the total number of polymorphisms and causal variants individually have small effects on the traits. We hypothesized that mapping molecular polymorphisms to genomic features such as genes and their gene ontology categories could increase the accuracy of genomic prediction models. We developed a genomic feature best linear unbiased prediction (GFBLUP) model that implements this strategy and applied it to three quantitative traits (startle response, starvation resistance, and chill coma recovery) in the unrelated, sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel. Our results indicate that subsetting markers based on genomic features increases the predictive ability relative to the standard genomic best linear unbiased prediction (GBLUP) model. Both models use all markers, but GFBLUP allows differential weighting of the individual genetic marker relationships, whereas GBLUP weighs the genetic marker relationships equally. Simulation studies show that it is possible to further increase the accuracy of genomic prediction for complex traits using this model, provided the genomic features are enriched for causal variants. Our GFBLUP model using prior information on genomic features enriched for causal variants can increase the accuracy of genomic predictions in populations of unrelated individuals and provides a formal statistical framework for leveraging and evaluating information across multiple experimental studies to provide novel insights into the genetic architecture of complex traits. PMID:27235308
Measuring causal perception: connections to representational momentum?

PubMed

Choi, Hoon; Scholl, Brian J

2006-01-01

In a collision between two objects, we can perceive not only low-level properties, such as color and motion, but also the seemingly high-level property of causality. It has proven difficult, however, to measure causal perception in a quantitatively rigorous way which goes beyond perceptual reports. Here we focus on the possibility of measuring perceived causality using the phenomenon of representational momentum (RM). Recent studies suggest a relationship between causal perception and RM, based on the fact that RM appears to be attenuated for causally 'launched' objects. This is explained by appeal to the visual expectation that a 'launched' object is inert and thus should eventually cease its movement after a collision, without a source of self-propulsion. We first replicated these demonstrations, and then evaluated this alleged connection by exploring RM for different types of displays, including the contrast between causal launching and non-causal 'passing'. These experiments suggest that the RM-attenuation effect is not a pure measure of causal perception, but rather may reflect lower-level spatiotemporal correlates of only some causal displays. We conclude by discussing the strengths and pitfalls of various methods of measuring causal perception.
A Children of Twins Study of parental divorce and offspring psychopathology.

PubMed

D'Onofrio, Brian M; Turkheimer, Eric; Emery, Robert E; Maes, Hermine H; Silberg, Judy; Eaves, Lindon J

2007-07-01

Although parental divorce is associated with increased substance use and internalizing problems, experiencing the separation of one's parents may not cause these outcomes. The relations may be due to genetic or environmental selection factors, characteristics that lead to both marital separation and offspring functioning. We used the Children of Twins (CoT) Design to explore whether unmeasured genetic or environmental factors related to the twin parent, and measured characteristics of both parents, account for the association between parental divorce and offspring substance use and internalizing problems. The association between parental divorce and offspring substance use problems remained robust when controlling for genetic and environmental risk from the twin parent associated with parental divorce, and measured characteristics of both parents. The results do not prove, but are consistent with, a causal connection. In contrast, the analyses suggest that shared genetic liability in parents and their offspring accounts for the increased risk of internalizing problems in adult offspring from divorced families. The study illustrates that unmeasured genetic and environmental selection factors must be considered when studying parental divorce. In explaining associations between parental divorce and young-adult adjustment, our evidence suggests that selection versus causal mechanisms may operate differently for substance abuse (a causal relation) and internalizing problems (an artifact of selection). The CoT design only controls for the genetic and environmental characteristics of one parent; thus, additional genetically informed analyses are needed.
Lectures on Dispersion Theory

DOE R&D Accomplishments Database

Salam, A.

1956-04-01

Lectures with mathematical analysis are given on Dispersion Theory and Causality and Dispersion Relations for Pion-nucleon Scattering. The appendix includes the S-matrix in terms of Heisenberg Operators. (F. S.)
How to establish causality in epilepsy surgery.

PubMed

Asano, Eishi; Brown, Erik C; Juhász, Csaba

2013-09-01

Focality in electro-clinical or neuroimaging data often motivates epileptologists to consider epilepsy surgery in patients with medically-uncontrolled seizures, while not all focal findings are causally associated with the generation of epileptic seizures. With the help of Hill's criteria, we have discussed how to establish causality in the context of the presurgical evaluation of epilepsy. The strengths of EEG include the ability to determine the temporal relationship between cerebral activities and clinical events; thus, scalp video-EEG is necessary in the evaluation of the majority of surgical candidates. The presence of associated ictal discharges can confirm the epileptic nature of a particular spell and whether an observed neuroimaging abnormality is causally associated with the epileptic seizure. Conversely, one should be aware that scalp EEG has a limited spatial resolution and sometimes exhibits propagated epileptiform discharges more predominantly than in situ discharges generated at the seizure-onset zone. Intraoperative or extraoperative electrocorticography (ECoG) is utilized when noninvasive presurgical evaluation, including anatomical and functional neuroimaging, fails to determine the margin between the presumed epileptogenic zone and eloquent cortex. Retrospective as well as prospective studies have reported that complete resection of the seizure-onset zone on ECoG was associated with a better seizure outcome, but not all patients became seizure-free following such resective surgery. Some retrospective studies suggested that resection of sites showing high-frequency oscillations (HFOs) at >80Hz on interictal or ictal ECoG was associated with a better seizure outcome. Others reported that functionally-important areas may generate HFOs of a physiological nature during rest as well as sensorimotor and cognitive tasks. Resection of sites showing task-related augmentation of HFOs has been reported to indeed result in functional loss following surgery. Thus, some but not all sites showing interictal HFOs are causally associated with seizure generation. Furthermore, evidence suggests that some task-related HFOs can be transiently suppressed by the prior occurrence of interictal spikes. The significance of interictal HFOs should be assessed by taking into account the eloquent cortex, seizure-onset zone, and cortical lesions. Video-EEG and ECoG generally provide useful but still limited information to establish causality in presurgical evaluation. A comprehensive assessment of data derived from multiple modalities is ultimately required for successful management. Copyright © 2013 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.
How to establish causality in epilepsy surgery

PubMed Central

Asano, Eishi; Brown, Erik C; Juhász, Csaba

2013-01-01

Focality in electro-clinical or neuroimaging data often motivates epileptologists to consider epilepsy surgery in patients with medically-uncontrolled seizures, while not all focal findings are causally associated with the generation of epileptic seizures. With the help of Hill's criteria, we have discussed how to establish causality in the context of the presurgical evaluation of epilepsy. The strengths of EEG include the ability to determine the temporal relationship between cerebral activities and clinical events; thus, scalp video-EEG is necessary in the evaluation of the majority of surgical candidates. The presence of associated ictal discharges can confirm the epileptic nature of a particular spell and whether an observed neuroimaging abnormality is causally associated with the epileptic seizure. Conversely, one should be aware that scalp EEG has a limited spatial resolution and sometimes exhibits propagated epileptiform discharges more predominantly than in situ discharges generated at the seizure-onset zone. Intraoperative or extraoperative electrocorticography (ECoG) is utilized when noninvasive presurgical evaluation, including anatomical and functional neuroimaging, fails to determine the margin between the presumed epileptogenic zone and eloquent cortex. Retrospective as well as prospective studies have reported that complete resection of the seizure-onset zone on ECoG was associated with a better seizure outcome, but not all patients became seizure-free following such resective surgery. Some retrospective studies suggested that resection of sites showing high-frequency oscillations (HFOs) at >80 Hz on interictal or ictal ECoG was associated with a better seizure outcome. Others reported that functionally-important areas may generate HFOs of a physiological nature during rest as well as sensorimotor and cognitive tasks. Resection of sites showing task-related augmentation of HFOs has been reported to indeed result in functional loss following surgery. Thus, some but not all sites showing interictal HFOs are causally associated with seizure generation. Furthermore, evidence suggests that some task-related HFOs can be transiently suppressed by the prior occurrence of interictal spikes. The significance of interictal HFOs should be assessed by taking into account the eloquent cortex, seizure-onset zone, and cortical lesions. Video-EEG and ECoG generally provide useful but still limited information to establish causality in presurgical evaluation. A comprehensive assessment of data derived from multiple modalities is ultimately required for successful management. PMID:23684007
Workload management and geographic disorientation in aviation incidents: A review of the ASRS data base

NASA Technical Reports Server (NTRS)

Williams, Henry P.; Tham, Mingpo; Wickens, Christopher D.

1993-01-01

NASA's Aviation Safety Reporting System (ASRS) incident reports are reviewed in two related areas: pilots' failures to appropriately manage tasks, and breakdowns in geographic orientation. Examination of 51 relevant reports on task management breakdowns revealed that altitude busts and inappropriate runway usee were the most frequently reported consequences. Task management breakdowns appeared to occur at all levels of expertise, and prominent causal factors were related to breakdowns in crew communications, over-involvement with the flight management system and, for small (general aviation) aircraft, preoccupation with weather. Analysis of the 83 cases of geographic disorientation suggested that these too occurred at all levels of pilot experience. With regard to causal factors, a majority was related to poor cockpit resource management, in which inattention led to a loss of geographic awareness. Other leading causes were related to poor weather and poor decision making. The potential of the ASRS database for contributing to research and design issues is addressed.
Disease causality extraction based on lexical semantics and document-clause frequency from biomedical literature.

PubMed

Lee, Dong-Gi; Shin, Hyunjung

2017-05-18

Recently, research on human disease network has succeeded and has become an aid in figuring out the relationship between various diseases. In most disease networks, however, the relationship between diseases has been simply represented as an association. This representation results in the difficulty of identifying prior diseases and their influence on posterior diseases. In this paper, we propose a causal disease network that implements disease causality through text mining on biomedical literature. To identify the causality between diseases, the proposed method includes two schemes: the first is the lexicon-based causality term strength, which provides the causal strength on a variety of causality terms based on lexicon analysis. The second is the frequency-based causality strength, which determines the direction and strength of causality based on document and clause frequencies in the literature. We applied the proposed method to 6,617,833 PubMed literature, and chose 195 diseases to construct a causal disease network. From all possible pairs of disease nodes in the network, 1011 causal pairs of 149 diseases were extracted. The resulting network was compared with that of a previous study. In terms of both coverage and quality, the proposed method showed outperforming results; it determined 2.7 times more causalities and showed higher correlation with associated diseases than the existing method. This research has novelty in which the proposed method circumvents the limitations of time and cost in applying all possible causalities in biological experiments and it is a more advanced text mining technique by defining the concepts of causality term strength.
Genetic evidence of a causal effect of insulin resistance on branched-chain amino acid levels.

PubMed

Mahendran, Yuvaraj; Jonsson, Anna; Have, Christian T; Allin, Kristine H; Witte, Daniel R; Jørgensen, Marit E; Grarup, Niels; Pedersen, Oluf; Kilpeläinen, Tuomas O; Hansen, Torben

2017-05-01

Fasting plasma levels of branched-chain amino acids (BCAAs) are associated with insulin resistance, but it remains unclear whether there is a causal relation between the two. We aimed to disentangle the causal relations by performing a Mendelian randomisation study using genetic variants associated with circulating BCAA levels and insulin resistance as instrumental variables. We measured circulating BCAA levels in blood plasma by NMR spectroscopy in 1,321 individuals from the ADDITION-PRO cohort. We complemented our analyses by using previously published genome-wide association study (GWAS) results from the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) (n = 46,186) and from a GWAS of serum BCAA levels (n = 24,925). We used a genetic risk score (GRS), calculated using ten established fasting serum insulin associated variants, as an instrumental variable for insulin resistance. A GRS of three variants increasing circulating BCAA levels was used as an instrumental variable for circulating BCAA levels. Fasting plasma BCAA levels were associated with higher HOMA-IR in ADDITION-PRO (β 0.137 [95% CI 0.08, 0.19] p = 6 × 10 -7 ). However, the GRS for circulating BCAA levels was not associated with fasting insulin levels or HOMA-IR in ADDITION-PRO (β -0.011 [95% CI -0.053, 0.032] p = 0.6 and β -0.011 [95% CI -0.054, 0.031] p = 0.6, respectively) or in GWAS results for HOMA-IR from MAGIC (β for valine-increasing GRS -0.012 [95% CI -0.069, 0.045] p = 0.7). By contrast, the insulin-resistance-increasing GRS was significantly associated with increased BCAA levels in ADDITION-PRO (β 0.027 [95% CI 0.005, 0.048] p = 0.01) and in GWAS results for serum BCAA levels (β 1.22 [95% CI 0.71, 1.73] p = 4 × 10 -6 , β 0.96 [95% CI 0.45, 1.47] p = 3 × 10 -4 , and β 0.67 [95% CI 0.16, 1.18] p = 0.01 for isoleucine, leucine and valine levels, respectively) and instrumental variable analyses in ADDITION-PRO indicated that HOMA-IR is causally related to higher circulating fasting BCAA levels (β 0.73 [95% CI 0.26, 1.19] p = 0.002). Our results suggest that higher BCAA levels do not have a causal effect on insulin resistance while increased insulin resistance drives higher circulating fasting BCAA levels.
Verification of the causal relationship between subchronic exposures to dinotefuran and depression-related phenotype in juvenile mice.

PubMed

Takada, Tadashi; Yoneda, Naoki; Hirano, Tetsushi; Yanai, Shogo; Yamamoto, Anzu; Mantani, Youhei; Yokoyama, Toshifumi; Kitagawa, Hiroshi; Tabuchi, Yoshiaki; Hoshi, Nobuhiko

2018-04-27

It has been suggested that an increase in the use of pesticides affects neurodevelopment, but there has been no animal experiment showing a causal relation between neonicotinoid pesticides (NNs) and depression. We examined whether dinotefuran (DIN), the most widely used NN in Japan, induces depression. Male mice were administered DIN between 3 and 8 weeks of age, referring to the no-observed-effect level (NOEL). The mice were then subjected to a tail suspension test (TST) and a forced swimming test (FST). After these tests, their brains were dissected for immunohistochemical analyses of serotonin (5-HT). Antidepressant activity in TST and no decrease in 5-HT-positive cells were observed. The subchronic exposure to DIN alone in juvenile male mice may not cause depression-like indication.
Constructing and Adapting Causal and Formative Measures of Family Settings: The HOME Inventory as Illustration

PubMed Central

Bradley, Robert H.

2015-01-01

Measures of the home environment are frequently used in studies of children’s development. This review provides information on indices composed of causal and formative indicators (the kind of indicators often used to capture salient aspects of family environments) and to suggest approaches that may be useful in constructing such measures for diverse populations. The HOME Inventory is used to illustrate challenges scholars face in determining what to include in useful measures of family settings. To that end, a cross-cultural review of research on relations among HOME, family context, and child outcomes is presented. The end of the review offers a plan for how best to further research on relations between the home environment and child development for diverse populations. PMID:26997978
Absence of a Causal Relationship between Auxin-Induced Growth and Changes in the Content of Ascorbic and Dehydroascorbic Acids in Excised Plant Tissues 12

PubMed Central

Lin, C. Y.; Key, Joe L.

1967-01-01

The data reported indicate that the oxidation-reduction balance of the ascorbic acid system is not causally related to the auxin-regulation of cell elongation. There was no shift in the ascorbic acid (AA) to dehydroascorbic acid (DHA) ratio with growth-promoting concentration of auxin in several plant tissues. The AA to DHA ratio was experimentally increased without altering the growth rate. Inhibition of growth by supra-optimal auxin was associated with a decrease in the AA to DHA ratio. Since the AA to DHA ratio was lowered by EDTA treatment without altering growth, it seems unlikely that the decrease in the AA to DHA ratio related to the inhibition of growth by high levels of auxin. PMID:16656564
Depression and genetic causal attribution of epilepsy in multiplex epilepsy families.

PubMed

Sorge, Shawn T; Hesdorffer, Dale C; Phelan, Jo C; Winawer, Melodie R; Shostak, Sara; Goldsmith, Jeff; Chung, Wendy K; Ottman, Ruth

2016-10-01

Rapid advances in genetic research and increased use of genetic testing have increased the emphasis on genetic causes of epilepsy in patient encounters. Research in other disorders suggests that genetic causal attributions can influence patients' psychological responses and coping strategies, but little is known about how epilepsy patients and their relatives will respond to genetic attributions of epilepsy. We investigated the possibility that among members of families containing multiple individuals with epilepsy, depression, the most frequent psychiatric comorbidity in the epilepsies, might be related to the perception that epilepsy has a genetic cause. A self-administered survey was completed by 417 individuals in 104 families averaging 4 individuals with epilepsy per family. Current depression was measured with the Patient Health Questionnaire. Genetic causal attribution was assessed by three questions addressing the following: perceived likelihood of having an epilepsy-related mutation, perceived role of genetics in causing epilepsy in the family, and (in individuals with epilepsy) perceived influence of genetics in causing the individual's epilepsy. Relatives without epilepsy were asked about their perceived chance of developing epilepsy in the future, compared with the average person. Prevalence of current depression was 14.8% in 182 individuals with epilepsy, 6.5% in 184 biologic relatives without epilepsy, and 3.9% in 51 individuals married into the families. Among individuals with epilepsy, depression was unrelated to genetic attribution. Among biologic relatives without epilepsy, however, prevalence of depression increased with increasing perceived chance of having an epilepsy-related mutation (p = 0.02). This association was not mediated by perceived future epilepsy risk among relatives without epilepsy. Depression is associated with perceived likelihood of carrying an epilepsy-related mutation among individuals without epilepsy in families containing multiple affected individuals. This association should be considered when addressing mental health issues in such families. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.
Study on localization of epileptic focus based on causality analysis

NASA Astrophysics Data System (ADS)

Shan, Shaojie; Li, Hanjun; Tang, Xiaoying

2018-05-01

In this paper, we considered that the ECoG signal contain abundant pathological information, which can be used for the localization of epileptic focus before epileptic seizures in 1-2 mins. In order to validate this hypothesis, cutting the ECoG into three stages: before seizure, seizure and after seizure, then through using Granger causality algorithm, PSI causality algorithm, Transfer Entropy causality algorithm at different stages of epilepsy ECoG, we were able to do the causality analysis of ECoG data. The results have shown that there is significant difference with the causality value of the epileptic focus area in before seizure, seizure and after seizure. An increase is in the causality value of each channel during epileptic seizure. After epileptic seizure, the causality between the channels showed a downward trend, but the difference was not obvious. The difference of the causality provides a reliable technical method to assist the clinical diagnosis of epileptic focus.
Exploring individual differences in preschoolers' causal stance.

PubMed

Alvarez, Aubry; Booth, Amy E

2016-03-01

Preschoolers, as a group, are highly attuned to causality, and this attunement is known to facilitate memory, learning, and problem solving. However, recent work reveals substantial individual variability in the strength of children's "causal stance," as demonstrated by their curiosity about and preference for new causal information. In this study, we explored the coherence and short-term stability of individual differences in children's causal stance. We also began to investigate the origins of this variability, focusing particularly on the potential role of mothers' explanatory talk in shaping the causal stance of their children. Two measures of causal stance correlated with each other, as well as themselves across time. Both also revealed internal consistency of response. The strength of children's causal stance also correlated with mother's responses on the same tasks and the frequency with which mothers emphasized causality during naturalistic joint activities with their children. Implications for theory and practice are discussed. (c) 2016 APA, all rights reserved).
Repeated causal decision making.

PubMed

Hagmayer, York; Meder, Björn

2013-01-01

Many of our decisions refer to actions that have a causal impact on the external environment. Such actions may not only allow for the mere learning of expected values or utilities but also for acquiring knowledge about the causal structure of our world. We used a repeated decision-making paradigm to examine what kind of knowledge people acquire in such situations and how they use their knowledge to adapt to changes in the decision context. Our studies show that decision makers' behavior is strongly contingent on their causal beliefs and that people exploit their causal knowledge to assess the consequences of changes in the decision problem. A high consistency between hypotheses about causal structure, causally expected values, and actual choices was observed. The experiments show that (a) existing causal hypotheses guide the interpretation of decision feedback, (b) consequences of decisions are used to revise existing causal beliefs, and (c) decision makers use the experienced feedback to induce a causal model of the choice situation even when they have no initial causal hypotheses, which (d) enables them to adapt their choices to changes of the decision problem. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
Outcomes of role stress: a multisample constructive replication.

PubMed

Kemery, E R; Bedeian, A G; Mossholder, K W; Touliatos, J

1985-06-01

Responses from four separate samples of accountants and hospital employees provided a constructive replication of the Bedeian and Armenakis (1981) model of the causal nexus between role stress and selected outcome variables. We investigated the relationship between both role ambiguity and role conflict--as specific forms of role stress--and job-related tension, job satisfaction, and propensity to leave, using LISREL IV, a technique capable of providing statistical data for a hypothesized population model, as well as for specific causal paths. Results, which support the Bedeian and Armenakis model, are discussed in light of previous research.

Detectability of Granger causality for subsampled continuous-time neurophysiological processes.

PubMed

Barnett, Lionel; Seth, Anil K

2017-01-01

Granger causality is well established within the neurosciences for inference of directed functional connectivity from neurophysiological data. These data usually consist of time series which subsample a continuous-time biophysiological process. While it is well known that subsampling can lead to imputation of spurious causal connections where none exist, less is known about the effects of subsampling on the ability to reliably detect causal connections which do exist. We present a theoretical analysis of the effects of subsampling on Granger-causal inference. Neurophysiological processes typically feature signal propagation delays on multiple time scales; accordingly, we base our analysis on a distributed-lag, continuous-time stochastic model, and consider Granger causality in continuous time at finite prediction horizons. Via exact analytical solutions, we identify relationships among sampling frequency, underlying causal time scales and detectability of causalities. We reveal complex interactions between the time scale(s) of neural signal propagation and sampling frequency. We demonstrate that detectability decays exponentially as the sample time interval increases beyond causal delay times, identify detectability "black spots" and "sweet spots", and show that downsampling may potentially improve detectability. We also demonstrate that the invariance of Granger causality under causal, invertible filtering fails at finite prediction horizons, with particular implications for inference of Granger causality from fMRI data. Our analysis emphasises that sampling rates for causal analysis of neurophysiological time series should be informed by domain-specific time scales, and that state-space modelling should be preferred to purely autoregressive modelling. On the basis of a very general model that captures the structure of neurophysiological processes, we are able to help identify confounds, and offer practical insights, for successful detection of causal connectivity from neurophysiological recordings. Copyright Â© 2016 Elsevier B.V. All rights reserved.
Interactions of information transfer along separable causal paths

NASA Astrophysics Data System (ADS)

Jiang, Peishi; Kumar, Praveen

2018-04-01

Complex systems arise as a result of interdependences between multiple variables, whose causal interactions can be visualized in a time-series graph. Transfer entropy and information partitioning approaches have been used to characterize such dependences. However, these approaches capture net information transfer occurring through a multitude of pathways involved in the interaction and as a result mask our ability to discern the causal interaction within a subgraph of interest through specific pathways. We build on recent developments of momentary information transfer along causal paths proposed by Runge [Phys. Rev. E 92, 062829 (2015), 10.1103/PhysRevE.92.062829] to develop a framework for quantifying information partitioning along separable causal paths. Momentary information transfer along causal paths captures the amount of information transfer between any two variables lagged at two specific points in time. Our approach expands this concept to characterize the causal interaction in terms of synergistic, unique, and redundant information transfer through separable causal paths. Through a graphical model, we analyze the impact of the separable and nonseparable causal paths and the causality structure embedded in the graph as well as the noise effect on information partitioning by using synthetic data generated from two coupled logistic equation models. Our approach can provide a valuable reference for an autonomous information partitioning along separable causal paths which form a causal subgraph influencing a target.
Mise à jour technique sur la physiologie et l'évaluation du liquide amniotique.

PubMed

Lim, Kenneth I; Butt, Kimberly; Naud, Kentia; Smithies, Mila

2017-01-01

RéSULTATS: SOURCES DES DONNéES PROBANTES: Nous avons effectué une recherche sur MEDLINE et Kfinder pour trouver des articles pertinents, puis avons examiné les bibliographies des articles retenus, notamment des revues Cochrane et des articles de revue récents. Les données recueillies ont été analysées par le Comité d'imagerie diagnostique de la Société des obstétriciens et gynécologues du Canada. Les recommandations ont été classées selon les critères établis par le Groupe d'étude canadien sur les soins de santé préventifs (Tableau 1). AVANTAGES, DéSAVANTAGES ET COûTS: En obstétrique moderne, l'évaluation du volume de liquide amniotique par échographie fait partie intégrante de l'évaluation fœtale. Toute anomalie donne lieu à une intervention obstétricale et à des examens approfondis. Au Canada, il n'existe aucune définition normalisée associée à l'estimation du volume de liquide amniotique ni aucune approche d'évaluation uniforme. Plusieurs essais randomisés semblent indiquer que l'évaluation de poche unique, plutôt que de plusieurs poches (indice de liquide amniotique), diminue les interventions obstétricales sans augmenter l'incidence de résultats indésirables. La littérature récente laisse croire que des changements légers, mais détectables du liquide amniotique peuvent survenir une heure ou deux suivant des manœuvres physiologiques normales, ce qui pourrait expliquer la variabilité et le manque de cohérence des résultats d'évaluations répétées en peu de temps et ainsi créer de la confusion et donner lieu à d'autres examens. Le présent article vise à décrire les limites de l'évaluation du volume de liquide amniotique, à promouvoir une méthode d'évaluation normalisée et à proposer un ensemble commun de définitions. DéCLARATIONS SOMMAIRES: RECOMMANDATION. Copyright © 2017. Published by Elsevier Inc.
Sufficiency and Necessity Assumptions in Causal Structure Induction

ERIC Educational Resources Information Center

Mayrhofer, Ralf; Waldmann, Michael R.

2016-01-01

Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when…
Different Kinds of Causality in Event Cognition

ERIC Educational Resources Information Center

Radvansky, Gabriel A.; Tamplin, Andrea K.; Armendarez, Joseph; Thompson, Alexis N.

2014-01-01

Narrative memory is better for information that is more causally connected and occurs at event boundaries, such as a causal break. However, it is unclear whether there are common or distinct influences of causality. For the event boundaries that arise as a result of causal breaks, the events that follow may subsequently become more causally…
Foundational perspectives on causality in large-scale brain networks

NASA Astrophysics Data System (ADS)

Mannino, Michael; Bressler, Steven L.

2015-12-01

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain.
Foundational perspectives on causality in large-scale brain networks.

PubMed

Mannino, Michael; Bressler, Steven L

2015-12-01

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain. Copyright © 2015 Elsevier B.V. All rights reserved.
Causality networks from multivariate time series and application to epilepsy.

PubMed

Siggiridou, Elsa; Koutlis, Christos; Tsimpiris, Alkiviadis; Kimiskidis, Vasilios K; Kugiumtzis, Dimitris

2015-08-01

Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. For this, realizations on high dimensional coupled dynamical systems are considered and the performance of the Granger causality measures is evaluated, seeking for the measures that form networks closest to the true network of the dynamical system. In particular, the comparison focuses on Granger causality measures that reduce the state space dimension when many variables are observed. Further, the linear and nonlinear Granger causality measures of dimension reduction are compared to a standard Granger causality measure on electroencephalographic (EEG) recordings containing episodes of epileptiform discharges.
Learning about causes from people and about people as causes: probabilistic models and social causal reasoning.

PubMed

Buchsbaum, Daphna; Seiver, Elizabeth; Bridgers, Sophie; Gopnik, Alison

2012-01-01

A major challenge children face is uncovering the causal structure of the world around them. Previous research on children's causal inference has demonstrated their ability to learn about causal relationships in the physical environment using probabilistic evidence. However, children must also learn about causal relationships in the social environment, including discovering the causes of other people's behavior, and understanding the causal relationships between others' goal-directed actions and the outcomes of those actions. In this chapter, we argue that social reasoning and causal reasoning are deeply linked, both in the real world and in children's minds. Children use both types of information together and in fact reason about both physical and social causation in fundamentally similar ways. We suggest that children jointly construct and update causal theories about their social and physical environment and that this process is best captured by probabilistic models of cognition. We first present studies showing that adults are able to jointly infer causal structure and human action structure from videos of unsegmented human motion. Next, we describe how children use social information to make inferences about physical causes. We show that the pedagogical nature of a demonstrator influences children's choices of which actions to imitate from within a causal sequence and that this social information interacts with statistical causal evidence. We then discuss how children combine evidence from an informant's testimony and expressed confidence with evidence from their own causal observations to infer the efficacy of different potential causes. We also discuss how children use these same causal observations to make inferences about the knowledge state of the social informant. Finally, we suggest that psychological causation and attribution are part of the same causal system as physical causation. We present evidence that just as children use covariation between physical causes and their effects to learn physical causal relationships, they also use covaration between people's actions and the environment to make inferences about the causes of human behavior.
Italian regional health system structure and expected cancer survival.

PubMed

Vercelli, Marina; Lillini, Roberto; Quaglia, Alberto; Capocaccia, Riccardo

2014-01-01

Few studies deal with the association of socioeconomic and health system resource variables with cancer survival at the Italian regional level, where the greatest number of decisions about social and health policies and resource allocations are taken. The present study aimed to describe the causal relationships between socioeconomic and health system resource factors and regional cancer survival and to compute the expected cancer survival at provincial, regional and area levels. Age-standardized relative survival at 5 years from diagnosis of cases incident in 1995-1998 and followed up to 2004 were derived by gender for 11 sites from the Italian Association of Cancer Registries data bank. The socioeconomic and health system resource variables, describing at a regional level the macro-economy, demography, labor market, and health resources for 1995-2005, came from the Health for All database. A principal components factor analysis was applied to the socioeconomic and health system resource variables. For every site, linear regression models were computed considering the relative survival at 5 years as a dependent variable and the principal components factor analysis factors as independent variables. The factors described the socioeconomic and health-related features of the regional systems and were causally related to the characteristics of the patient taken in charge. The models built by the factors allowed computation of the expected relative survival at 5 years with very good concordance with those observed at regional, macro-regional and national levels. In the regions without any cancer registry, survival was coherent with that of neighboring regions with similar socioeconomic and health system resources characteristics. The models highlighted the causal correlations between socioeconomic and health system resources and cancer survival, suggesting that they could be good evaluation tools for the efficiency of the resources allocation and use.
Relations among maternal socialization, effortful control, and maladjustment in early childhood.

PubMed

Eisenberg, Nancy; Spinrad, Tracy L; Eggum, Natalie M; Silva, Kassondra M; Reiser, Mark; Hofer, Claire; Smith, Cynthia L; Gaertner, Bridget M; Kupfer, Anne; Popp, Tierney; Michalik, Nicole

2010-08-01

In a sample of 18-, 30-, and 42-month-olds, the relations among parenting, effortful control (EC), and maladjustment were examined. Parenting was assessed with mothers' reports and observations; EC was measured with mothers' and caregivers' reports, as well as a behavioral task; and externalizing and internalizing symptoms were assessed with parents' and caregivers' reports. Although 18-month unsupportive (vs. supportive) parenting negatively predicted EC at 30 months, when the stability of these variables was taken into account, there was no evidence of additional potentially causal relations between these two constructs. Although EC was negatively related to both internalizing and externalizing problems within all three ages as well as across 1 year, EC did not predict maladjustment once the stability of the constructs and within time covariation between the constructs were taken into account. In addition, externalizing problems at 30 months negatively predicted EC at 42 months, and internalizing problems at 30 months positively predicted EC at 42 months, but only when the effects of externalizing on EC were controlled. The findings are discussed in terms of the reasons for the lack of causal relations over time.
Immittance Data Validation by Kramers‐Kronig Relations – Derivation and Implications

PubMed Central

2017-01-01

Abstract Explicitly based on causality, linearity (superposition) and stability (time invariance) and implicit on continuity (consistency), finiteness (convergence) and uniqueness (single valuedness) in the time domain, Kramers‐Kronig (KK) integral transform (KKT) relations for immittances are derived as pure mathematical constructs in the complex frequency domain using the two‐sided (bilateral) Laplace integral transform (LT) reduced to the Fourier domain for sufficiently rapid exponential decaying, bounded immittances. Novel anti KK relations are also derived to distinguish LTI (linear, time invariant) systems from non‐linear, unstable and acausal systems. All relations can be used to test KK transformability on the LTI principles of linearity, stability and causality of measured and model data by Fourier transform (FT) in immittance spectroscopy (IS). Also, integral transform relations are provided to estimate (conjugate) immittances at zero and infinite frequency particularly useful to normalise data and compare data. Also, important implications for IS are presented and suggestions for consistent data analysis are made which generally apply likewise to complex valued quantities in many fields of engineering and natural sciences. PMID:29577007
Development of an automated assessment tool for MedWatch reports in the FDA adverse event reporting system.

PubMed

Han, Lichy; Ball, Robert; Pamer, Carol A; Altman, Russ B; Proestel, Scott

2017-09-01

As the US Food and Drug Administration (FDA) receives over a million adverse event reports associated with medication use every year, a system is needed to aid FDA safety evaluators in identifying reports most likely to demonstrate causal relationships to the suspect medications. We combined text mining with machine learning to construct and evaluate such a system to identify medication-related adverse event reports. FDA safety evaluators assessed 326 reports for medication-related causality. We engineered features from these reports and constructed random forest, L1 regularized logistic regression, and support vector machine models. We evaluated model accuracy and further assessed utility by generating report rankings that represented a prioritized report review process. Our random forest model showed the best performance in report ranking and accuracy, with an area under the receiver operating characteristic curve of 0.66. The generated report ordering assigns reports with a higher probability of medication-related causality a higher rank and is significantly correlated to a perfect report ordering, with a Kendall's tau of 0.24 ( P = .002). Our models produced prioritized report orderings that enable FDA safety evaluators to focus on reports that are more likely to contain valuable medication-related adverse event information. Applying our models to all FDA adverse event reports has the potential to streamline the manual review process and greatly reduce reviewer workload. Published by Oxford University Press on behalf of the American Medical Informatics Association 2017. This work is written by US Government employees and is in the public domain in the United States.
Intellectual and Developmental Disabilities in Kinshasa, Democratic Republic of the Congo: Causality and Implications for Resilience and Support

ERIC Educational Resources Information Center

Aldersey, Heather M.; Turnbull, H. Rutherford, III; Turnbull, Ann P.

2014-01-01

This article reports results of a 7-month qualitative study on intellectual and related developmental disabilities in Kinshasa, Democratic Republic of the Congo, particularly as they relate to the causes and meaning of intellectual and developmental disabilities (IDD). This study raises important questions related to the understanding of…
The influence of linguistic and cognitive factors on the time course of verb-based implicit causality.

PubMed

Koornneef, Arnout; Dotlačil, Jakub; van den Broek, Paul; Sanders, Ted

2016-01-01

In three eye-tracking experiments the influence of the Dutch causal connective "want" (because) and the working memory capacity of readers on the usage of verb-based implicit causality was examined. Experiments 1 and 2 showed that although a causal connective is not required to activate implicit causality information during reading, effects of implicit causality surfaced more rapidly and were more pronounced when a connective was present in the discourse than when it was absent. In addition, Experiment 3 revealed that-in contrast to previous claims-the activation of implicit causality is not a resource-consuming mental operation. Moreover, readers with higher and lower working memory capacities behaved differently in a dual-task situation. Higher span readers were more likely to use implicit causality when they had all their working memory resources at their disposal. Lower span readers showed the opposite pattern as they were more likely to use the implicit causality cue in the case of an additional working memory load. The results emphasize that both linguistic and cognitive factors mediate the impact of implicit causality on text comprehension. The implications of these results are discussed in terms of the ongoing controversies in the literature-that is, the focusing-integration debate and the debates on the source of implicit causality.
Knowing Who Dunnit: Infants Identify the Causal Agent in an Unseen Causal Interaction

ERIC Educational Resources Information Center

Saxe, Rebecca; Tzelnic, Tania; Carey, Susan

2007-01-01

Preverbal infants can represent the causal structure of events, including distinguishing the agentive and receptive roles and categorizing entities according to stable causal dispositions. This study investigated how infants combine these 2 kinds of causal inference. In Experiments 1 and 2, 9.5-month-olds used the position of a human hand or a…
Causal Systems Categories: Differences in Novice and Expert Categorization of Causal Phenomena

ERIC Educational Resources Information Center

Rottman, Benjamin M.; Gentner, Dedre; Goldwater, Micah B.

2012-01-01

We investigated the understanding of causal systems categories--categories defined by common causal structure rather than by common domain content--among college students. We asked students who were either novices or experts in the physical sciences to sort descriptions of real-world phenomena that varied in their causal structure (e.g., negative…
They Work Together to Roar: Kindergartners' Understanding of an Interactive Causal Task

ERIC Educational Resources Information Center

Solis, S. Lynneth; Grotzer, Tina A.

2016-01-01

The aim of this study was to investigate kindergartners' exploration of interactive causality during their play with a pair of toy sound blocks. Interactive causality refers to a type of causal pattern in which two entities interact to produce a causal force, as in particle attraction and symbiotic relationships. Despite being prevalent in nature,…
Context and Time in Causal Learning: Contingency and Mood Dependent Effects

PubMed Central

Msetfi, Rachel M.; Wade, Caroline; Murphy, Robin A.

2013-01-01

Defining cues for instrumental causality are the temporal, spatial and contingency relationships between actions and their effects. In this study, we carried out a series of causal learning experiments that systematically manipulated time and context in positive and negative contingency conditions. In addition, we tested participants categorized as non-dysphoric and mildly dysphoric because depressed mood has been shown to affect the processing of all these causal cues. Findings showed that causal judgements made by non-dysphoric participants were contextualized at baseline and were affected by the temporal spacing of actions and effects only with generative, but not preventative, contingency relationships. Participants categorized as dysphoric made less contextualized causal ratings at baseline but were more sensitive than others to temporal manipulations across the contingencies. These effects were consistent with depression affecting causal learning through the effects of slowed time experience on accrued exposure to the context in which causal events took place. Taken together, these findings are consistent with associative approaches to causal judgement. PMID:23691147
Phase Transitions in Living Neural Networks

NASA Astrophysics Data System (ADS)

Williams-Garcia, Rashid Vladimir

Our nervous systems are composed of intricate webs of interconnected neurons interacting in complex ways. These complex interactions result in a wide range of collective behaviors with implications for features of brain function, e.g., information processing. Under certain conditions, such interactions can drive neural network dynamics towards critical phase transitions, where power-law scaling is conjectured to allow optimal behavior. Recent experimental evidence is consistent with this idea and it seems plausible that healthy neural networks would tend towards optimality. This hypothesis, however, is based on two problematic assumptions, which I describe and for which I present alternatives in this thesis. First, critical transitions may vanish due to the influence of an environment, e.g., a sensory stimulus, and so living neural networks may be incapable of achieving "critical" optimality. I develop a framework known as quasicriticality, in which a relative optimality can be achieved depending on the strength of the environmental influence. Second, the power-law scaling supporting this hypothesis is based on statistical analysis of cascades of activity known as neuronal avalanches, which conflate causal and non-causal activity, thus confounding important dynamical information. In this thesis, I present a new method to unveil causal links, known as causal webs, between neuronal activations, thus allowing for experimental tests of the quasicriticality hypothesis and other practical applications.

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