The Biological Categorizations Made by 4 and 5-Year-Old Children: The Role of Feature Type versus Their Causal Status

ERIC Educational Resources Information Center

Meunier, Benjamin; Cordier, Francoise

2008-01-01

The present study here investigated the role of the causal status of features and feature type in biological categorizations by young children. Study 1 showed that 5-year-olds are more strongly influenced by causal features than effect features. 4-year-olds exhibit no such tendency. There, therefore, appears to be a conceptual change between the…

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

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

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.

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.

[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.

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 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.

[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.

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.

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

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.

Causality re-established.

PubMed

D'Ariano, Giacomo Mauro

2018-07-13

Causality has never gained the status of a 'law' or 'principle' in physics. Some recent literature has even popularized the false idea that causality is a notion that should be banned from theory. Such misconception relies on an alleged universality of the reversibility of the laws of physics, based either on the determinism of classical theory, or on the multiverse interpretation of quantum theory, in both cases motivated by mere interpretational requirements for realism of the theory. Here, I will show that a properly defined unambiguous notion of causality is a theorem of quantum theory, which is also a falsifiable proposition of the theory. Such a notion of causality appeared in the literature within the framework of operational probabilistic theories. It is a genuinely theoretical notion, corresponding to establishing a definite partial order among events, in the same way as we do by using the future causal cone on Minkowski space. The notion of causality is logically completely independent of the misidentified concept of 'determinism', and, being a consequence of quantum theory, is ubiquitous in physics. In addition, as classical theory can be regarded as a restriction of quantum theory, causality holds also in the classical case, although the determinism of the theory trivializes it. I then conclude by arguing that causality naturally establishes an arrow of time. This implies that the scenario of the 'block Universe' and the connected 'past hypothesis' are incompatible with causality, and thus with quantum theory: they are both doomed to remain mere interpretations and, as such, are not falsifiable, similar to the hypothesis of 'super-determinism'.This article is part of a discussion meeting issue 'Foundations of quantum mechanics and their impact on contemporary society'. © 2018 The Author(s).

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.

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…

Are bruxism and the bite causally related?

PubMed

Lobbezoo, F; Ahlberg, J; Manfredini, D; Winocur, E

2012-07-01

In the dental profession, the belief that bruxism and dental (mal-)occlusion ('the bite') are causally related is widespread. The aim of this review was to critically assess the available literature on this topic. A PubMed search of the English-language literature, using the query 'Bruxism [Majr] AND (Dental Occlusion [Majr] OR Malocclusion [Majr])', yielded 93 articles, of which 46 papers were finally included in the present review*. Part of the included publications dealt with the possible associations between bruxism and aspects of occlusion, from which it was concluded that neither for occlusal interferences nor for factors related to the anatomy of the oro-facial skeleton, there is any evidence available that they are involved in the aetiology of bruxism. Instead, there is a growing awareness of other factors (viz. psychosocial and behavioural ones) being important in the aetiology of bruxism. Another part of the included papers assessed the possible mediating role of occlusion between bruxism and its purported consequences (e.g. tooth wear, loss of periodontal tissues, and temporomandibular pain and dysfunction). Even though most dentists agree that bruxism may have several adverse effects on the masticatory system, for none of these purported adverse effects, evidence for a mediating role of occlusion and articulation has been found to date. Hence, based on this review, it should be concluded that to date, there is no evidence whatsoever for a causal relationship between bruxism and the bite. © 2012 Blackwell Publishing Ltd.

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.

Causality and headache triggers

PubMed Central

Turner, Dana P.; Smitherman, Todd A.; Martin, Vincent T.; Penzien, Donald B.; Houle, Timothy T.

2013-01-01

Objective The objective of this study was to explore the conditions necessary to assign causal status to headache triggers. Background The term “headache trigger” is commonly used to label any stimulus that is assumed to cause headaches. However, the assumptions required for determining if a given stimulus in fact has a causal-type relationship in eliciting headaches have not been explicated. Methods A synthesis and application of Rubin’s Causal Model is applied to the context of headache causes. From this application the conditions necessary to infer that one event (trigger) causes another (headache) are outlined using basic assumptions and examples from relevant literature. Results Although many conditions must be satisfied for a causal attribution, three basic assumptions are identified for determining causality in headache triggers: 1) constancy of the sufferer; 2) constancy of the trigger effect; and 3) constancy of the trigger presentation. A valid evaluation of a potential trigger’s effect can only be undertaken once these three basic assumptions are satisfied during formal or informal studies of headache triggers. Conclusions Evaluating these assumptions is extremely difficult or infeasible in clinical practice, and satisfying them during natural experimentation is unlikely. Researchers, practitioners, and headache sufferers are encouraged to avoid natural experimentation to determine the causal effects of headache triggers. Instead, formal experimental designs or retrospective diary studies using advanced statistical modeling techniques provide the best approaches to satisfy the required assumptions and inform causal statements about headache triggers. PMID:23534872

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.

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

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…

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.

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.

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…

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.

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…

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

Are Lowered Socioeconomic Circumstances Causally Related to Tooth Loss? A Natural Experiment Involving the 2011 Great East Japan Earthquake.

PubMed

Matsuyama, Yusuke; Aida, Jun; Tsuboya, Toru; Hikichi, Hiroyuki; Kondo, Katsunori; Kawachi, Ichiro; Osaka, Ken

2017-07-01

Oral health status is correlated with socioeconomic status. However, the causal nature of the relationship is not established. Here we describe a natural experiment involving deteriorating socioeconomic circumstances following exposure to the 2011 Great East Japan Earthquake and Tsunami. We investigated the relationship between subjective economic deterioration and housing damage due to the disaster and tooth loss in a cohort of community-dwelling residents (n = 3,039), from whom we obtained information about socioeconomic status and health status in 2010 (i.e., predating the disaster). A follow-up survey was performed in 2013 (postdisaster), and 82.1% of the 4,380 eligible survivors responded. We estimated the impact of subjective economic deterioration and housing damage due to the disaster on tooth loss by fitting an instrumental variable probit model. Subjective economic deterioration and housing damage due to the disaster were significantly associated with 8.1% and 1.7% increases in the probability of tooth loss (probit coefficients were 0.469 (95% confidence interval: 0.065, 0.872) and 0.103 (95% confidence interval: 0.011, 0.196), respectively). In this natural experiment, we confirmed the causal relationship between deteriorating socioeconomic circumstances and tooth loss. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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

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

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.

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.

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.

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

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].

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.…

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.

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.…

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

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…

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…

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 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.

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

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.

The causal nature of the association between neighborhood deprivation and drug abuse: a prospective national Swedish co-relative control study

PubMed Central

Kendler, K. S.; Ohlsson, H.; Sundquist, K.; Sundquist, J.

2014-01-01

Background Risk for drug abuse (DA) is strongly associated with neighborhood social deprivation (SD). However, the causal nature of this relationship is unclear. Method Three Swedish population-based cohorts were followed up over 5 years for incident registration of DA in medical, legal or pharmacy records. In each cohort, we examined the SD–DA association, controlling carefully for individual socio-economic status (SES) with multiple measures, in the entire sample and among pairs of first cousins, paternal and maternal half-siblings, full siblings and monozygotic (MZ) twins discordant for SD exposure. The number of informative relative pairs ranged from 6366 to 166208. Results In all cohorts, SD was prospectively related to risk for incident DA. In relative pairs discordant for SD exposure, the SD–DA association was similar to that seen in the entire population in cousins, half-siblings, full siblings and MZ twins. Eliminating subjects who were residentially unstable or had DA in the first two follow-up years did not alter this pattern. When divided by age, in the youngest groups, the SD–DA association was weaker in siblings than in the entire population. Conclusions Across three cohorts, controlling for individual SES and confounding familial factors, SD prospectively predicted risk for incident DA registration. These results support the hypothesis that the SD–DA association is in part causal and unlikely to result entirely from personal attributes, which both increase risk for DA and cause selection into high SD environments. At least part of the SD–DA association arises because exposure to SD causes an increased risk of DA. PMID:25055172

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.

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.

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.

Causal imprinting in causal structure learning.

PubMed

Taylor, Eric G; Ahn, Woo-Kyoung

2012-11-01

Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C followed by evidence that B and C were independent given A, persisted in believing that B causes C. The authors term this difficulty in revising initially learned causal structures "causal imprinting." Throughout four experiments, causal imprinting was obtained using multiple dependent measures and control conditions. A Bayesian analysis showed that causal imprinting may be normative under some conditions, but causal imprinting also occurred in the current study when it was clearly non-normative. It is suggested that causal imprinting occurs due to the influence of prior knowledge on how reasoners interpret later evidence. Consistent with this view, when participants first viewed the evidence showing that B and C are independent given A, later evidence with only B and C did not lead to the belief that B causes C. Copyright © 2012 Elsevier Inc. All rights reserved.

Causal Imprinting in Causal Structure Learning

PubMed Central

Taylor, Eric G.; Ahn, Woo-kyoung

2012-01-01

Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C followed by evidence that B and C were independent given A, persisted in believing that B causes C. The authors term this difficulty in revising initially learned causal structures “causal imprinting.” Throughout four experiments, causal imprinting was obtained using multiple dependent measures and control conditions. A Bayesian analysis showed that causal imprinting may be normative under some conditions, but causal imprinting also occurred in the current study when it was clearly non-normative. It is suggested that causal imprinting occurs due to the influence of prior knowledge on how reasoners interpret later evidence. Consistent with this view, when participants first viewed the evidence showing that B and C are independent given A, later evidence with only B and C did not lead to the belief that B causes C. PMID:22859019

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

Evidence for a causal relationship between early exocrine pancreatic disease and cystic fibrosis-related diabetes: a Mendelian randomization study.

PubMed

Soave, David; Miller, Melissa R; Keenan, Katherine; Li, Weili; Gong, Jiafen; Ip, Wan; Accurso, Frank; Sun, Lei; Rommens, Johanna M; Sontag, Marci; Durie, Peter R; Strug, Lisa J

2014-06-01

Circulating immunoreactive trypsinogen (IRT), a biomarker of exocrine pancreatic disease in cystic fibrosis (CF), is elevated in most CF newborns. In those with severe CF transmembrane conductance regulator (CFTR) genotypes, IRT declines rapidly in the first years of life, reflecting progressive pancreatic damage. Consistent with this progression, a less elevated newborn IRT measure would reflect more severe pancreatic disease, including compromised islet compartments, and potentially increased risk of CF-related diabetes (CFRD). We show in two independent CF populations that a lower newborn IRT estimate is associated with higher CFRD risk among individuals with severe CFTR genotypes, and we provide evidence to support a causal relationship. Increased loge(IRT) at birth was associated with decreased CFRD risk in Canadian and Colorado samples (hazard ratio 0.30 [95% CI 0.15-0.61] and 0.39 [0.18-0.81], respectively). Using Mendelian randomization with the SLC26A9 rs7512462 genotype as an instrumental variable since it is known to be associated with IRT birth levels in the CF population, we provide evidence to support a causal contribution of exocrine pancreatic status on CFRD risk. Our findings suggest CFRD risk could be predicted in early life and that maintained ductal fluid flow in the exocrine pancreas could delay the onset of CFRD. © 2014 by the American Diabetes Association.

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

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

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.

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

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.

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 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

Causality

NASA Astrophysics Data System (ADS)

Pearl, Judea

2000-03-01

Written by one of the pre-eminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, philosophy, cognitive science, and the health and social sciences. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.

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.

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).

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

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

The Special Status of Actions in Causal Reasoning in Rats

ERIC Educational Resources Information Center

Leising, Kenneth J.; Wong, Jared; Waldmann, Michael R.; Blaisdell, Aaron P.

2008-01-01

A. P. Blaisdell, K. Sawa, K. J. Leising, and M. R. Waldmann (2006) reported evidence for causal reasoning in rats. After learning through Pavlovian observation that Event A (a light) was a common cause of Events X (an auditory stimulus) and F (food), rats predicted F in the test phase when they observed Event X as a cue but not when they generated…

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

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…

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.

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.

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.

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.

Comparison of relative and non-relative adoptive parent health status.

PubMed

Foli, Karen J; Lim, Eunjung; Sands, Laura P

2015-03-01

Across the United States, kinship parents, extended family members and close friends, render care to the 2.7 million children who have been removed from their birth parents' care. However, differences between relative and non-relative parents reported health statuses have not been explored. The National Survey of Adoptive Parents data were used to investigate the health status of relative (n = 469) and non-relative (n = 1,599) adoptive parents. Perceived happiness in the parent-child relationship and the parents' ability to cope appear to affect parental health status. Only non-related mothers of children younger than 6 years reported better emotional health than those mothers who were related to their children. With this exception, and despite caring for children who have a greater likelihood of abuse, neglect, and exposure to drugs and alcohol prior to birth, the reported health statuses of relative parents did not differ from non-relative parents. © The Author(s) 2013.

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.

Causal effects of socioeconomic status on central adiposity risks: Evidence using panel data from urban Mexico.

PubMed

Levasseur, Pierre

2015-07-01

Associated with overweight, obesity and chronic diseases, the nutrition transition process reveals important socioeconomic issues in Mexico. Using panel data from the Mexican Family Life Survey, the purpose of the study is to estimate the causal effect of household socioeconomic status (SES) on nutritional outcomes among urban adults. We divide the analysis into two steps. First, using a mixed clustering procedure, we distinguish four socioeconomic classes based on income, educational and occupational dimensions: (i) a poor class; (ii) a lower-middle class; (iii) an upper-middle class; (iv) a rich class. Second, using an econometric framework adapted to our study (the Hausman-Taylor estimator), we measure the impact of belonging to these socioeconomic groups on individual anthropometric indicators, based on the body-mass index (BMI) and the waist-to-height ratio (WHtR). Our results make several contributions: (i) we show that a new middle class, rising out of poverty, is the most exposed to the risks of adiposity; (ii) as individuals from the upper class seem to be fatter than individuals from the upper-middle class, we can reject the assumption of an inverted U-shaped relationship between socioeconomic and anthropometric status as commonly suggested in emerging economies; (iii) the influence of SES on central adiposity appears to be particularly strong for men. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

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…

Relational Aggressive Behaviour: The Contributions of Status Stress and Status Goals

ERIC Educational Resources Information Center

Flack, Tove

2017-01-01

Relational aggression is a destructive behaviour that increases during adolescence. In order to develop effective interventions aimed to combat relational aggression, there is an urgent need to study what motivates this behaviour. This study investigates the association between status stress, status goals, and relational aggressive behaviour in a…

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

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.

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

Granger causality revisited

PubMed Central

Friston, Karl J.; Bastos, André M.; Oswal, Ashwini; van Wijk, Bernadette; Richter, Craig; Litvak, Vladimir

2014-01-01

This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kernels prescribe the second-order statistics of their response to random fluctuations; characterised in terms of cross-spectral density, cross-covariance, autoregressive coefficients and directed transfer functions. These quantities in turn specify Granger causality — providing a direct (analytic) link between the parameters of a generative model and the expected Granger causality. We use this link to show that Granger causality measures based upon autoregressive models can become unreliable when the underlying dynamics is dominated by slow (unstable) modes — as quantified by the principal Lyapunov exponent. However, nonparametric measures based on causal spectral factors are robust to dynamical instability. We then demonstrate how both parametric and nonparametric spectral causality measures can become unreliable in the presence of measurement noise. Finally, we show that this problem can be finessed by deriving spectral causality measures from Volterra kernels, estimated using dynamic causal modelling. PMID:25003817

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…

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.

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.

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.

Whither Causal Models in the Neuroscience of ADHD?

ERIC Educational Resources Information Center

Coghill, Dave; Nigg, Joel; Rothenberger, Aribert; Sonuga-Barke, Edmund; Tannock, Rosemary

2005-01-01

In this paper we examine the current status of the science of ADHD from a theoretical point of view. While the field has reached the point at which a number of causal models have been proposed, it remains some distance away from demonstrating the viability of such models empirically. We identify a number of existing barriers and make proposals as…

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).

The cradle of causal reasoning: newborns' preference for physical causality.

PubMed

Mascalzoni, Elena; Regolin, Lucia; Vallortigara, Giorgio; Simion, Francesca

2013-05-01

Perception of mechanical (i.e. physical) causality, in terms of a cause-effect relationship between two motion events, appears to be a powerful mechanism in our daily experience. In spite of a growing interest in the earliest causal representations, the role of experience in the origin of this sensitivity is still a matter of dispute. Here, we asked the question about the innate origin of causal perception, never tested before at birth. Three experiments were carried out to investigate sensitivity at birth to some visual spatiotemporal cues present in a launching event. Newborn babies, only a few hours old, showed that they significantly preferred a physical causality event (i.e. Michotte's Launching effect) when matched to a delay event (i.e. a delayed launching; Experiment 1) or to a non-causal event completely identical to the causal one except for the order of the displacements of the two objects involved which was swapped temporally (Experiment 3). This preference for the launching event, moreover, also depended on the continuity of the trajectory between the objects involved in the event (Experiment 2). These results support the hypothesis that the human system possesses an early available, possibly innate basic mechanism to compute causality, such a mechanism being sensitive to the additive effect of certain well-defined spatiotemporal cues present in the causal event independently of any prior visual experience. © 2013 Blackwell Publishing Ltd.

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 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…

Health and Wealth of Elderly Couples: Causality Tests Using Dynamic Panel Data Models*

PubMed Central

Michaud, Pierre-Carl; van Soest, Arthur

2010-01-01

A positive relationship between socio-economic status (SES) and health, the “health-wealth gradient”, is repeatedly found in many industrialized countries. This study analyzes competing explanations for this gradient: causal effects from health to wealth (health causation) and causal effects from wealth to health (wealth or social causation). Using six biennial waves of couples aged 51–61 in 1992 from the U.S. Health and Retirement Study, we test for causality in panel data models incorporating unobserved heterogeneity and a lag structure supported by specification tests. In contrast to tests relying on models with only first order lags or without unobserved heterogeneity, these tests provide no evidence of causal wealth health effects. On the other hand, we find strong evidence of causal effects from both spouses’ health on household wealth. We also find an effect of the husband’s health on the wife’s mental health, but no other effects from one spouse’s health to health of the other spouse. PMID:18513809

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…

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.

Subjective Social Status and Cardiovascular Reactivity: An Experimental Examination

PubMed Central

Pieritz, Karoline; Süssenbach, Philipp; Rief, Winfried; Euteneuer, Frank

2016-01-01

The present experiment examined the causal influence of subjective social status (SSS) on variables related to cardiovascular health [i.e., blood pressure, heart rate variability (HRV)]. Participants were randomly assigned to one of two conditions involving a social comparison that either induced a temporary shift toward high SSS or toward low SSS. Cardiovascular variables were measured before (baseline), throughout, and after the manipulation (recovery). Participants in the low SSS condition had a significantly lower HRV during experimental manipulation than at baseline (p = 0.001). They also showed a significantly stronger HRV reactivity compared to participants in the high SSS condition (p = 0.027). Our results suggest that already temporary shifts of one's SSS have measureable effects on cardiovascular variables. They support the notion that social status plays a causal role in the development of cardiovascular disease. PMID:27486426

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 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.

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.

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

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).

Trust and health: testing the reverse causality hypothesis

PubMed Central

Giordano, Giuseppe Nicola; Lindström, Martin

2016-01-01

Background Social capital research has consistently shown positive associations between generalised trust and health outcomes over 2 decades. Longitudinal studies attempting to test causal relationships further support the theory that trust is an independent predictor of health. However, as the reverse causality hypothesis has yet to be empirically tested, a knowledge gap remains. The aim of this study, therefore, was to investigate if health status predicts trust. Methods Data employed in this study came from 4 waves of the British Household Panel Survey between years 2000 and 2007 (N=8114). The sample was stratified by baseline trust to investigate temporal relationships between prior self-rated health (SRH) and changes in trust. We used logistic regression models with random effects, as trust was expected to be more similar within the same individuals over time. Results From the ‘Can trust at baseline’ cohort, poor SRH at time (t−1) predicted low trust at time (t) (OR=1.38). Likewise, good health predicted high trust within the ‘Cannot’ trust cohort (OR=1.30). These patterns of positive association remained after robustness checks, which adjusted for misclassification of outcome (trust) status and the existence of other temporal pathways. Conclusions This study offers empirical evidence to support the circular nature of trust/health relationship. The stability of association between prior health status and changes in trust over time differed between cohorts, hinting at the existence of complex pathways rather than a simple positive feedback loop. PMID:26546287

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

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.

The influence of status on satisfaction with relative rewards

PubMed Central

Albrecht, Konstanze; von Essen, Emma; Fliessbach, Klaus; Falk, Armin

2013-01-01

This study investigates how induced relative status affects satisfaction with different relative payoffs. We find that participants with lower status are more satisfied with disadvantageous payoff inequalities than equal or higher status participants. In contrast, when receiving an advantageous payoff, status does not affect satisfaction. Our findings suggest that relative social status has important implications for the acceptance of income inequalities. PMID:24198802

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

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 attributions in Brazilian children's reasoning about health and illness.

PubMed

Boruchovitch, E; Mednick, B R

2000-10-01

At a time when a great number of diseases can be prevented by changing one's habits and life style, investigations have focused on understanding what adults and children believe to be desirable health practices and uncovering the factors associated with successful adherence to such practices. For these, causal attributions for health and illness were investigated among 96 Brazilian elementary school students. Ninety six subjects, aged 6 to 14, were interviewed individually and their causal attributions were assessed through 14 true-false items (e.g. people stay well [healthy] because they are lucky). The relationship between the children's causal attributions and demographic characteristics were also examined. Overall, the results were consistent with previous researches. "Taking care of oneself" was considered the most important cause of good health. "Viruses and germs" and "lack of self-care" were the most selected causes of illness. Analyses revealed significant relationship between subjects' causal attribution and their age, school grade level, socioeconomic status and gender. The study findings suggest that there may be more cross-cultural similarities than differences in children's causal attributions for health and illness. Finding ways to help individuals engage in appropriate preventive-maintenance health practices without developing an exaggerated notion that the individuals can control their own health and illness is a challenge which remains to be addressed by further research.

The serum uric acid concentration is not causally linked to diabetic nephropathy in type 1 diabetes.

PubMed

Ahola, Aila J; Sandholm, Niina; Forsblom, Carol; Harjutsalo, Valma; Dahlström, Emma; Groop, Per-Henrik

2017-05-01

Previous studies have shown a relationship between uric acid concentration and progression of renal disease. Here we studied causality between the serum uric acid concentration and progression of diabetic nephropathy in 3895 individuals with type 1 diabetes in the FinnDiane Study. The renal status was assessed with the urinary albumin excretion rate and estimated glomerular filtration rate (eGFR) at baseline and at the end of the follow-up. Based on previous genomewide association studies on serum uric acid concentration, 23 single nucleotide polymorphisms (SNPs) with good imputation quality were selected for the SNP score. This score was used to assess the causality between serum uric acid and renal complications using a Mendelian randomization approach. At baseline, the serum uric acid concentration was higher with worsening renal status. In multivariable Cox regression analyses, baseline serum uric acid concentration was not independently associated with progression of diabetic nephropathy over a mean follow-up of 7 years. However, over the same period, baseline serum uric acid was independently associated with the decline in eGFR. In the cross-sectional logistic regression analyses, the SNP score was associated with the serum uric acid concentration. Nevertheless, the Mendelian randomization showed no causality between uric acid and diabetic nephropathy, eGFR categories, or eGFR as a continuous variable. Thus, our results suggest that the serum uric acid concentration is not causally related to diabetic nephropathy but is a downstream marker of kidney damage. Copyright © 2016 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

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

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 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.

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.

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

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…

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

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.

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.

Epidemiological causality.

PubMed

Morabia, Alfredo

2005-01-01

Epidemiological methods, which combine population thinking and group comparisons, can primarily identify causes of disease in populations. There is therefore a tension between our intuitive notion of a cause, which we want to be deterministic and invariant at the individual level, and the epidemiological notion of causes, which are invariant only at the population level. Epidemiologists have given heretofore a pragmatic solution to this tension. Causal inference in epidemiology consists in checking the logical coherence of a causality statement and determining whether what has been found grossly contradicts what we think we already know: how strong is the association? Is there a dose-response relationship? Does the cause precede the effect? Is the effect biologically plausible? Etc. This approach to causal inference can be traced back to the English philosophers David Hume and John Stuart Mill. On the other hand, the mode of establishing causality, devised by Jakob Henle and Robert Koch, which has been fruitful in bacteriology, requires that in every instance the effect invariably follows the cause (e.g., inoculation of Koch bacillus and tuberculosis). This is incompatible with epidemiological causality which has to deal with probabilistic effects (e.g., smoking and lung cancer), and is therefore invariant only for the population.

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

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.

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.

University Students' Causal Conceptions about Social Mobility: Diverging Pathways for Believers in Personal Merit and Luck

ERIC Educational Resources Information Center

Shane, Jacob; Heckhausen, Jutta

2013-01-01

Many college students hold ambitious goals for upward social mobility via post-college careers. However, in the current economic recession such optimistic expectations are not a given. The present study examines how college students' current social status and beliefs in causal factors for socioeconomic status (SES) attainment lead to diverging…

[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.

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.

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.

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.

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.

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…

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.

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

Generalized causal mediation and path analysis: Extensions and practical considerations.

PubMed

Albert, Jeffrey M; Cho, Jang Ik; Liu, Yiying; Nelson, Suchitra

2018-01-01

Causal mediation analysis seeks to decompose the effect of a treatment or exposure among multiple possible paths and provide casually interpretable path-specific effect estimates. Recent advances have extended causal mediation analysis to situations with a sequence of mediators or multiple contemporaneous mediators. However, available methods still have limitations, and computational and other challenges remain. The present paper provides an extended causal mediation and path analysis methodology. The new method, implemented in the new R package, gmediation (described in a companion paper), accommodates both a sequence (two stages) of mediators and multiple mediators at each stage, and allows for multiple types of outcomes following generalized linear models. The methodology can also handle unsaturated models and clustered data. Addressing other practical issues, we provide new guidelines for the choice of a decomposition, and for the choice of a reference group multiplier for the reduction of Monte Carlo error in mediation formula computations. The new method is applied to data from a cohort study to illuminate the contribution of alternative biological and behavioral paths in the effect of socioeconomic status on dental caries in adolescence.

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…

Dominance rank causally affects personality and glucocorticoid regulation in female rhesus macaques

PubMed Central

Kohn, Jordan N.; Snyder-Mackler, Noah; Barreiro, Luis B.; Johnson, Zachary P.; Tung, Jenny; Wilson, Mark E.

2017-01-01

Low social status is frequently associated with heightened exposure to social stressors and altered glucocorticoid regulation by the hypothalamic-pituitary-adrenal (HPA) axis. Additionally, personality differences can affect how individuals behave in response to social conditions, and thus may aggravate or protect against the effects of low status on HPA function. Disentangling the relative importance of personality from the effects of the social environment on the HPA axis has been challenging, since social status can predict aspects of behavior, and both can remain stable across the lifespan. To do so here, we studied an animal model of social status and social behavior, the rhesus macaque (Macaca mulatta). We performed two sequential experimental manipulations of dominance rank (i.e., social status) in 45 adult females, allowing us to characterize personality and glucocorticoid regulation (based on sensitivity to the exogenous glucocorticoid dexamethasone) in each individual while she occupied two different dominance ranks. We identified two behavioral characteristics, termed ‘social approachability’ and ‘boldness,’ which were highly social status-dependent. Social approachability and a third dimension, anxiousness, were also associated with cortisol dynamics in low status females, suggesting that behavioral tendencies may sensitize individuals to the effects of low status on HPA axis function. Finally, we found that improvements in dominance rank increased dexamethasone-induced acute cortisol suppression and glucocorticoid negative feedback. Our findings indicate that social status causally affects both behavioral tendencies and glucocorticoid regulation, and that some behavioral tendencies also independently affect cortisol levels, beyond the effects of rank. Together, they highlight the importance of considering personality and social status together when investigating their effects on HPA axis function. PMID:27639059

Dominance rank causally affects personality and glucocorticoid regulation in female rhesus macaques.

PubMed

Kohn, Jordan N; Snyder-Mackler, Noah; Barreiro, Luis B; Johnson, Zachary P; Tung, Jenny; Wilson, Mark E

2016-12-01

Low social status is frequently associated with heightened exposure to social stressors and altered glucocorticoid regulation by the hypothalamic-pituitary-adrenal (HPA) axis. Additionally, personality differences can affect how individuals behave in response to social conditions, and thus may aggravate or protect against the effects of low status on HPA function. Disentangling the relative importance of personality from the effects of the social environment on the HPA axis has been challenging, since social status can predict aspects of behavior, and both can remain stable across the lifespan. To do so here, we studied an animal model of social status and social behavior, the rhesus macaque (Macaca mulatta). We performed two sequential experimental manipulations of dominance rank (i.e., social status) in 45 adult females, allowing us to characterize personality and glucocorticoid regulation (based on sensitivity to the exogenous glucocorticoid dexamethasone) in each individual while she occupied two different dominance ranks. We identified two behavioral characteristics, termed 'social approachability' and 'boldness,' which were highly social status-dependent. Social approachability and a third dimension, anxiousness, were also associated with cortisol dynamics in low status females, suggesting that behavioral tendencies may sensitize individuals to the effects of low status on HPA axis function. Finally, we found that improvements in dominance rank increased dexamethasone-induced acute cortisol suppression and glucocorticoid negative feedback. Our findings indicate that social status causally affects both behavioral tendencies and glucocorticoid regulation, and that some behavioral tendencies also independently affect cortisol levels, beyond the effects of rank. Together, they highlight the importance of considering personality and social status together when investigating their effects on HPA axis function. Copyright © 2016 Elsevier Ltd. All rights reserved.

Agency, time, and causality

PubMed Central

Widlok, Thomas

2014-01-01

Cognitive Scientists interested in causal cognition increasingly search for evidence from non-Western Educational Industrial Rich Democratic people but find only very few cross-cultural studies that specifically target causal cognition. This article suggests how information about causality can be retrieved from ethnographic monographs, specifically from ethnographies that discuss agency and concepts of time. Many apparent cultural differences with regard to causal cognition dissolve when cultural extensions of agency and personhood to non-humans are taken into account. At the same time considerable variability remains when we include notions of time, linearity and sequence. The article focuses on ethnographic case studies from Africa but provides a more general perspective on the role of ethnography in research on the diversity and universality of causal cognition. PMID:25414683

Structural Equations and Causal Explanations: Some Challenges for Causal SEM

ERIC Educational Resources Information Center

Markus, Keith A.

2010-01-01

One common application of structural equation modeling (SEM) involves expressing and empirically investigating causal explanations. Nonetheless, several aspects of causal explanation that have an impact on behavioral science methodology remain poorly understood. It remains unclear whether applications of SEM should attempt to provide complete…

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…

Causality discovery technology

NASA Astrophysics Data System (ADS)

Chen, M.; Ertl, T.; Jirotka, M.; Trefethen, A.; Schmidt, A.; Coecke, B.; Bañares-Alcántara, R.

2012-11-01

Causality is the fabric of our dynamic world. We all make frequent attempts to reason causation relationships of everyday events (e.g., what was the cause of my headache, or what has upset Alice?). We attempt to manage causality all the time through planning and scheduling. The greatest scientific discoveries are usually about causality (e.g., Newton found the cause for an apple to fall, and Darwin discovered natural selection). Meanwhile, we continue to seek a comprehensive understanding about the causes of numerous complex phenomena, such as social divisions, economic crisis, global warming, home-grown terrorism, etc. Humans analyse and reason causality based on observation, experimentation and acquired a priori knowledge. Today's technologies enable us to make observations and carry out experiments in an unprecedented scale that has created data mountains everywhere. Whereas there are exciting opportunities to discover new causation relationships, there are also unparalleled challenges to benefit from such data mountains. In this article, we present a case for developing a new piece of ICT, called Causality Discovery Technology. We reason about the necessity, feasibility and potential impact of such a technology.

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.

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

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…

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…

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.

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.

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…

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.

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

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.

Right external globus pallidus changes are associated with altered causal awareness in youth with depression

PubMed Central

Griffiths, K R; Lagopoulos, J; Hermens, D F; Hickie, I B; Balleine, B W

2015-01-01

Cognitive impairment is a functionally disabling feature of depression contributing to maladaptive decision-making, a loss of behavioral control and an increased disease burden. The ability to calculate the causal efficacy of ones actions in achieving specific goals is critical to normal decision-making and, in this study, we combined voxel-based morphometry (VBM), shape analysis and diffusion tensor tractography to investigate the relationship between cortical–basal ganglia structural integrity and such causal awareness in 43 young subjects with depression and 21 demographically similar healthy controls. Volumetric analysis determined a relationship between right pallidal size and sensitivity to the causal status of specific actions. More specifically, shape analysis identified dorsolateral surface vertices where an inward location was correlated with reduced levels of causal awareness. Probabilistic tractography revealed that affected parts of the pallidum were primarily connected with the striatum, dorsal thalamus and hippocampus. VBM did not reveal any whole-brain gray matter regions that correlated with causal awareness. We conclude that volumetric reduction within the indirect pathway involving the right dorsolateral pallidum is associated with reduced awareness of the causal efficacy of goal-directed actions in young depressed individuals. This causal awareness task allows for the identification of a functionally and biologically relevant subgroup to which more targeted cognitive interventions could be applied, potentially enhancing the long-term outcomes for these individuals. PMID:26440541

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.

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.

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.

Reasoning about Causal Relationships: Inferences on Causal Networks

PubMed Central

Rottman, Benjamin Margolin; Hastie, Reid

2013-01-01

Over the last decade, a normative framework for making causal inferences, Bayesian Probabilistic Causal Networks, has come to dominate psychological studies of inference based on causal relationships. The following causal networks—[X→Y→Z, X←Y→Z, X→Y←Z]—supply answers for questions like, “Suppose both X and Y occur, what is the probability Z occurs?” or “Suppose you intervene and make Y occur, what is the probability Z occurs?” In this review, we provide a tutorial for how normatively to calculate these inferences. Then, we systematically detail the results of behavioral studies comparing human qualitative and quantitative judgments to the normative calculations for many network structures and for several types of inferences on those networks. Overall, when the normative calculations imply that an inference should increase, judgments usually go up; when calculations imply a decrease, judgments usually go down. However, two systematic deviations appear. First, people’s inferences violate the Markov assumption. For example, when inferring Z from the structure X→Y→Z, people think that X is relevant even when Y completely mediates the relationship between X and Z. Second, even when people’s inferences are directionally consistent with the normative calculations, they are often not as sensitive to the parameters and the structure of the network as they should be. We conclude with a discussion of productive directions for future research. PMID:23544658

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.

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

Tropical Convective Transition Statistics and Causality in the Water Vapor–Precipitation Relation

DOE PAGES

Kuo, Yi-Hung; Neelin, J. David; Mechoso, C. Roberto

2017-03-09

Previous work by various authors has pointed to the role of lower-free-tropospheric humidity in affecting the onset of deep convection in the tropics. Empirical relationships between column water vapor (CWV) and precipitation have been inferred to result from these effects. Evidence from previous work has included deep convective conditional instability calculations for entraining plumes, in which the lower-free-tropospheric environment affects the onset of deep convection due to the differential impact on buoyancy of turbulent entrainment of dry versus moist air. The relationship between deep convection and water vapor is, however, a two-way interaction because convection also moistens the free troposphere.more » The present study adds an additional line of evidence toward fully establishing the causality of the precipitation–water vapor relationship. Parameter perturbation experiments using the coupled Community Earth System Model (CESM) with high-time-resolution output are analyzed for a set of statistics for the transition to deep convection, coordinated with observational diagnostics for the Green Ocean Amazon (GOAmazon) campaign and tropical western Pacific Atmospheric Radiation Measurement (ARM) sites. For low values of entrainment in the deep convective scheme, these statistics are radically altered and the observed pickup of precipitation with CWV is no longer seen. In addition to helping cement the dominant direction of causality in the fast-time-scale precipitation–CWV relationship, the results point to impacts of entrainment on the climatology. Because at low entrainment convection can fire before tropospheric moistening, the climatological values of relative humidity are lower than observed. These findings can be consequential to biases in simulated climate and to projections of climate change.« less

Tropical Convective Transition Statistics and Causality in the Water Vapor–Precipitation Relation

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

Kuo, Yi-Hung; Neelin, J. David; Mechoso, C. Roberto

Previous work by various authors has pointed to the role of lower-free-tropospheric humidity in affecting the onset of deep convection in the tropics. Empirical relationships between column water vapor (CWV) and precipitation have been inferred to result from these effects. Evidence from previous work has included deep convective conditional instability calculations for entraining plumes, in which the lower-free-tropospheric environment affects the onset of deep convection due to the differential impact on buoyancy of turbulent entrainment of dry versus moist air. The relationship between deep convection and water vapor is, however, a two-way interaction because convection also moistens the free troposphere.more » The present study adds an additional line of evidence toward fully establishing the causality of the precipitation–water vapor relationship. Parameter perturbation experiments using the coupled Community Earth System Model (CESM) with high-time-resolution output are analyzed for a set of statistics for the transition to deep convection, coordinated with observational diagnostics for the Green Ocean Amazon (GOAmazon) campaign and tropical western Pacific Atmospheric Radiation Measurement (ARM) sites. For low values of entrainment in the deep convective scheme, these statistics are radically altered and the observed pickup of precipitation with CWV is no longer seen. In addition to helping cement the dominant direction of causality in the fast-time-scale precipitation–CWV relationship, the results point to impacts of entrainment on the climatology. Because at low entrainment convection can fire before tropospheric moistening, the climatological values of relative humidity are lower than observed. These findings can be consequential to biases in simulated climate and to projections of climate change.« less

Causality and causal inference in epidemiology: the need for a pluralistic approach

PubMed Central

Vandenbroucke, Jan P; Broadbent, Alex; Pearce, Neil

2016-01-01

Abstract Causal inference based on a restricted version of the potential outcomes approach reasoning is assuming an increasingly prominent place in the teaching and practice of epidemiology. The proposed concepts and methods are useful for particular problems, but it would be of concern if the theory and practice of the complete field of epidemiology were to become restricted to this single approach to causal inference. Our concerns are that this theory restricts the questions that epidemiologists may ask and the study designs that they may consider. It also restricts the evidence that may be considered acceptable to assess causality, and thereby the evidence that may be considered acceptable for scientific and public health decision making. These restrictions are based on a particular conceptual framework for thinking about causality. In Section 1, we describe the characteristics of the restricted potential outcomes approach (RPOA) and show that there is a methodological movement which advocates these principles, not just for solving particular problems, but as ideals for which epidemiology as a whole should strive. In Section 2, we seek to show that the limitation of epidemiology to one particular view of the nature of causality is problematic. In Section 3, we argue that the RPOA is also problematic with regard to the assessment of causality. We argue that it threatens to restrict study design choice, to wrongly discredit the results of types of observational studies that have been very useful in the past and to damage the teaching of epidemiological reasoning. Finally, in Section 4 we set out what we regard as a more reasonable ‘working hypothesis’ as to the nature of causality and its assessment: pragmatic pluralism. PMID:26800751

Redundant variables and Granger causality

NASA Astrophysics Data System (ADS)

Angelini, L.; de Tommaso, M.; Marinazzo, D.; Nitti, L.; Pellicoro, M.; Stramaglia, S.

2010-03-01

We discuss the use of multivariate Granger causality in presence of redundant variables: the application of the standard analysis, in this case, leads to under estimation of causalities. Using the un-normalized version of the causality index, we quantitatively develop the notions of redundancy and synergy in the frame of causality and propose two approaches to group redundant variables: (i) for a given target, the remaining variables are grouped so as to maximize the total causality and (ii) the whole set of variables is partitioned to maximize the sum of the causalities between subsets. We show the application to a real neurological experiment, aiming to a deeper understanding of the physiological basis of abnormal neuronal oscillations in the migraine brain. The outcome by our approach reveals the change in the informational pattern due to repetitive transcranial magnetic stimulations.

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.

A Causal Relation between Bioluminescence and Oxygen to Quantify the Cell Niche

PubMed Central

Lambrechts, Dennis; Roeffaers, Maarten; Goossens, Karel; Hofkens, Johan; Van de Putte, Tom; Schrooten, Jan; Van Oosterwyck, Hans

2014-01-01

Bioluminescence imaging assays have become a widely integrated technique to quantify effectiveness of cell-based therapies by monitoring fate and survival of transplanted cells. To date these assays are still largely qualitative and often erroneous due to the complexity and dynamics of local micro-environments (niches) in which the cells reside. Here, we report, using a combined experimental and computational approach, on oxygen that besides being a critical niche component responsible for cellular energy metabolism and cell-fate commitment, also serves a primary role in regulating bioluminescent light kinetics. We demonstrate the potential of an oxygen dependent Michaelis-Menten relation in quantifying intrinsic bioluminescence intensities by resolving cell-associated oxygen gradients from bioluminescent light that is emitted from three-dimensional (3D) cell-seeded hydrogels. Furthermore, the experimental and computational data indicate a strong causal relation of oxygen concentration with emitted bioluminescence intensities. Altogether our approach demonstrates the importance of oxygen to evolve towards quantitative bioluminescence and holds great potential for future microscale measurement of oxygen tension in an easily accessible manner. PMID:24840204

A causal relation between bioluminescence and oxygen to quantify the cell niche.

PubMed

Lambrechts, Dennis; Roeffaers, Maarten; Goossens, Karel; Hofkens, Johan; Vande Velde, Greetje; Van de Putte, Tom; Schrooten, Jan; Van Oosterwyck, Hans

2014-01-01

Bioluminescence imaging assays have become a widely integrated technique to quantify effectiveness of cell-based therapies by monitoring fate and survival of transplanted cells. To date these assays are still largely qualitative and often erroneous due to the complexity and dynamics of local micro-environments (niches) in which the cells reside. Here, we report, using a combined experimental and computational approach, on oxygen that besides being a critical niche component responsible for cellular energy metabolism and cell-fate commitment, also serves a primary role in regulating bioluminescent light kinetics. We demonstrate the potential of an oxygen dependent Michaelis-Menten relation in quantifying intrinsic bioluminescence intensities by resolving cell-associated oxygen gradients from bioluminescent light that is emitted from three-dimensional (3D) cell-seeded hydrogels. Furthermore, the experimental and computational data indicate a strong causal relation of oxygen concentration with emitted bioluminescence intensities. Altogether our approach demonstrates the importance of oxygen to evolve towards quantitative bioluminescence and holds great potential for future microscale measurement of oxygen tension in an easily accessible manner.

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

Youth's Causal Beliefs About Success: Socioeconomic Differences and Prediction of Early Career Development.

PubMed

Kay, Joseph S; Shane, Jacob; Heckhausen, Jutta

2017-10-01

Youth's career attainment is associated with socioeconomic background, but may also be related to their beliefs about causes of success. Relationships between 17-year-olds' socioeconomic status (SES) and causal beliefs about success, and whether these beliefs predict career attainment after completing a vocational or university degree were examined using data from the German Socio-Economic Panel Study (n = 997, 48.5% female). Youth with higher SES parents and those who attended higher levels of high schools were less likely to believe that success in society is due to external causes, but SES was unrelated to the belief that success is due to personal merit or ability. Youth who believe that success is due to external causes attained lower income, occupational prestige, and job autonomy, and slower increases in income over time. There were also significant indirect effects of youth's parents' SES and their own high school levels on career attainment through such external causal beliefs; merit beliefs, by contrast, were largely unrelated to career attainment. These results suggest that beliefs about external causes of success may uniquely contribute to the transmission and maintenance of SES across generations and over time.

Experimental test of nonlocal causality.

PubMed

Ringbauer, Martin; Giarmatzi, Christina; Chaves, Rafael; Costa, Fabio; White, Andrew G; Fedrizzi, Alessandro

2016-08-01

Explaining observations in terms of causes and effects is central to empirical science. However, correlations between entangled quantum particles seem to defy such an explanation. This implies that some of the fundamental assumptions of causal explanations have to give way. We consider a relaxation of one of these assumptions, Bell's local causality, by allowing outcome dependence: a direct causal influence between the outcomes of measurements of remote parties. We use interventional data from a photonic experiment to bound the strength of this causal influence in a two-party Bell scenario, and observational data from a Bell-type inequality test for the considered models. Our results demonstrate the incompatibility of quantum mechanics with a broad class of nonlocal causal models, which includes Bell-local models as a special case. Recovering a classical causal picture of quantum correlations thus requires an even more radical modification of our classical notion of cause and effect.

[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.

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…

Causality and causal inference in epidemiology: the need for a pluralistic approach.

PubMed

Vandenbroucke, Jan P; Broadbent, Alex; Pearce, Neil

2016-12-01

Causal inference based on a restricted version of the potential outcomes approach reasoning is assuming an increasingly prominent place in the teaching and practice of epidemiology. The proposed concepts and methods are useful for particular problems, but it would be of concern if the theory and practice of the complete field of epidemiology were to become restricted to this single approach to causal inference. Our concerns are that this theory restricts the questions that epidemiologists may ask and the study designs that they may consider. It also restricts the evidence that may be considered acceptable to assess causality, and thereby the evidence that may be considered acceptable for scientific and public health decision making. These restrictions are based on a particular conceptual framework for thinking about causality. In Section 1, we describe the characteristics of the restricted potential outcomes approach (RPOA) and show that there is a methodological movement which advocates these principles, not just for solving particular problems, but as ideals for which epidemiology as a whole should strive. In Section 2, we seek to show that the limitation of epidemiology to one particular view of the nature of causality is problematic. In Section 3, we argue that the RPOA is also problematic with regard to the assessment of causality. We argue that it threatens to restrict study design choice, to wrongly discredit the results of types of observational studies that have been very useful in the past and to damage the teaching of epidemiological reasoning. Finally, in Section 4 we set out what we regard as a more reasonable 'working hypothesis' as to the nature of causality and its assessment: pragmatic pluralism. © The Author 2016. Published by Oxford University Press on behalf of the International Epidemiological Association.

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.

Empirical evaluation of the conceptual model underpinning a regional aquatic long-term monitoring program using causal modelling

USGS Publications Warehouse

Irvine, Kathryn M.; Miller, Scott; Al-Chokhachy, Robert K.; Archer, Erik; Roper, Brett B.; Kershner, Jeffrey L.

2015-01-01

Conceptual models are an integral facet of long-term monitoring programs. Proposed linkages between drivers, stressors, and ecological indicators are identified within the conceptual model of most mandated programs. We empirically evaluate a conceptual model developed for a regional aquatic and riparian monitoring program using causal models (i.e., Bayesian path analysis). We assess whether data gathered for regional status and trend estimation can also provide insights on why a stream may deviate from reference conditions. We target the hypothesized causal pathways for how anthropogenic drivers of road density, percent grazing, and percent forest within a catchment affect instream biological condition. We found instream temperature and fine sediments in arid sites and only fine sediments in mesic sites accounted for a significant portion of the maximum possible variation explainable in biological condition among managed sites. However, the biological significance of the direct effects of anthropogenic drivers on instream temperature and fine sediments were minimal or not detected. Consequently, there was weak to no biological support for causal pathways related to anthropogenic drivers’ impact on biological condition. With weak biological and statistical effect sizes, ignoring environmental contextual variables and covariates that explain natural heterogeneity would have resulted in no evidence of human impacts on biological integrity in some instances. For programs targeting the effects of anthropogenic activities, it is imperative to identify both land use practices and mechanisms that have led to degraded conditions (i.e., moving beyond simple status and trend estimation). Our empirical evaluation of the conceptual model underpinning the long-term monitoring program provided an opportunity for learning and, consequently, we discuss survey design elements that require modification to achieve question driven monitoring, a necessary step in the practice of

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.

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 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

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.

Nutritional status according to Mini Nutritional Assessment is related to functional status in geriatric patients--independent of health status.

PubMed

Schrader, E; Baumgärtel, C; Gueldenzoph, H; Stehle, P; Uter, W; Sieber, C C; Volkert, D

2014-03-01

The aim of this study was to investigate the relationship between nutritional and functional status in acute geriatric patients including mobility and considering health status. Cross-sectional study. Hospital. 205 geriatric patients (median age 82.0 (IQR: 80-86) years, 69.3% women). Nutritional status was determined by Mini Nutritional Assessment (MNA) and patients were categorized as well-nourished (≥ 24 points), at risk of malnutrition (17-23.5 points) or as malnourished (< 17 points). Functional status was determined by Barthel Index (BI) and Timed 'Up and Go' Test (TUG) and related to MNA categories. Using binary multiple logistic regression the impact of nutritional status on functional status was examined, adjusted for health status. 60.3% of the patients were at risk of malnutrition and 29.8% were malnourished. Ability to perform basic activities of daily living (ADL) decreased with declining nutritional status. The proportion of patients unable to perform the TUG increased with worsening of nutritional status (45.0% vs. 50.4% vs. 77.0%, p<0.01). After adjusting for age, gender, number of diagnoses, disease severity and cognitive function, a higher MNA score significantly lowered the risk of being dependent in ADL (OR 0.85, 95 % CI 0.77-0.94) and inability to perform the TUG (OR 0.90, 95 % CI 0.82-0.99). Nutritional status according to MNA was related to ADL as well as to mobility in acute geriatric patients. This association remained after adjusting for health status.

Experimental test of nonlocal causality

PubMed Central

Ringbauer, Martin; Giarmatzi, Christina; Chaves, Rafael; Costa, Fabio; White, Andrew G.; Fedrizzi, Alessandro

2016-01-01

Explaining observations in terms of causes and effects is central to empirical science. However, correlations between entangled quantum particles seem to defy such an explanation. This implies that some of the fundamental assumptions of causal explanations have to give way. We consider a relaxation of one of these assumptions, Bell’s local causality, by allowing outcome dependence: a direct causal influence between the outcomes of measurements of remote parties. We use interventional data from a photonic experiment to bound the strength of this causal influence in a two-party Bell scenario, and observational data from a Bell-type inequality test for the considered models. Our results demonstrate the incompatibility of quantum mechanics with a broad class of nonlocal causal models, which includes Bell-local models as a special case. Recovering a classical causal picture of quantum correlations thus requires an even more radical modification of our classical notion of cause and effect. PMID:27532045

Are attentional bias and memory bias for negative words causally related?

PubMed

Blaut, Agata; Paulewicz, Borysław; Szastok, Marta; Prochwicz, Katarzyna; Koster, Ernst

2013-09-01

In cognitive theories of depression, processing biases are assumed to be partly responsible for the onset and maintenance of mood disorders. Despite a wealth of studies examining the relation between depression and individual biases (at the level of attention, interpretation, and memory), little is known about relationships between different biases. The purpose of the present study was to assess if attentional bias is causally related to memory bias. 71 participants were randomly assigned to a control (n = 37) or attentional training group (n = 34). The attentional manipulation was followed by an explicit, intentional memory task during which novel neutral, negative, and positive words were presented. It was found that individuals with elevated depression score trained to orient away from negative words did not display a memory bias for negative words (adjectives) whereas similar individuals displayed this memory bias in the control condition. Generalization of the findings is limited because of the short study time frame and specific nature of the memory task. These results indicate that altering attentional bias can influence elaborative processing of emotional material and that this bias could be one of the causes of mood congruent memory in depression. Copyright © 2013 Elsevier Ltd. All rights reserved.

Causal conditionals and counterfactuals

PubMed Central

Frosch, Caren A.; Byrne, Ruth M.J.

2012-01-01

Causal counterfactuals e.g., ‘if the ignition key had been turned then the car would have started’ and causal conditionals e.g., ‘if the ignition key was turned then the car started’ are understood by thinking about multiple possibilities of different sorts, as shown in six experiments using converging evidence from three different types of measures. Experiments 1a and 1b showed that conditionals that comprise enabling causes, e.g., ‘if the ignition key was turned then the car started’ primed people to read quickly conjunctions referring to the possibility of the enabler occurring without the outcome, e.g., ‘the ignition key was turned and the car did not start’. Experiments 2a and 2b showed that people paraphrased causal conditionals by using causal or temporal connectives (because, when), whereas they paraphrased causal counterfactuals by using subjunctive constructions (had…would have). Experiments 3a and 3b showed that people made different inferences from counterfactuals presented with enabling conditions compared to none. The implications of the results for alternative theories of conditionals are discussed. PMID:22858874

Causal Discovery of Dynamic Systems

ERIC Educational Resources Information Center

Voortman, Mark

2010-01-01

Recently, several philosophical and computational approaches to causality have used an interventionist framework to clarify the concept of causality [Spirtes et al., 2000, Pearl, 2000, Woodward, 2005]. The characteristic feature of the interventionist approach is that causal models are potentially useful in predicting the effects of manipulations.…

[Health behaviour and changes in health behaviour - are education and social status relevant?].

PubMed

Altenhöner, T; Philippi, M; Böcken, J

2014-01-01

Individual health behaviour counts as an important factor for health status. A healthier lifestyle substantially contributes to better health. People burdened with lower health and with lower socio-economic status could benefit notably. So far it is not known exhaustively to what extent education and social status contribute to changes in health behaviour and which motifs play a decisive role. Based on cross-sectional data from the seventh wave of the "Gesundheitsmonitor", Bertelsmann Foundation, (n=1 436), the influence of social status and education on health behaviour and changes in behaviour was analysed. Specific health behaviour correlates with level of education and socio-economic status. In contrast, regarding health behaviour changes in the last 12 months prior to survey, no social class- or education-specific effect was found. Age, health status as well as fears and wishes in relation to health seem to be important causalities for changes of health-related behaviour. Interventions to foster healthy lifestyles should include class differences in specific health-related behaviour and personal reasons for behavioural changes. © Georg Thieme Verlag KG Stuttgart · New York.

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.

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…

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.

Does sufficient evidence exist to support a causal association between vitamin D status and cardiovascular disease risk? An assessment using Hill's criteria for causality.

PubMed

Weyland, Patricia G; Grant, William B; Howie-Esquivel, Jill

2014-09-02

Serum 25-hydroxyvitamin D (25(OH)D) levels have been found to be inversely associated with both prevalent and incident cardiovascular disease (CVD) risk factors; dyslipidemia, hypertension and diabetes mellitus. This review looks for evidence of a causal association between low 25(OH)D levels and increased CVD risk. We evaluated journal articles in light of Hill's criteria for causality in a biological system. The results of our assessment are as follows. Strength of association: many randomized controlled trials (RCTs), prospective and cross-sectional studies found statistically significant inverse associations between 25(OH)D levels and CVD risk factors. Consistency of observed association: most studies found statistically significant inverse associations between 25(OH)D levels and CVD risk factors in various populations, locations and circumstances. Temporality of association: many RCTs and prospective studies found statistically significant inverse associations between 25(OH)D levels and CVD risk factors. Biological gradient (dose-response curve): most studies assessing 25(OH)D levels and CVD risk found an inverse association exhibiting a linear biological gradient. Plausibility of biology: several plausible cellular-level causative mechanisms and biological pathways may lead from a low 25(OH)D level to increased risk for CVD with mediators, such as dyslipidemia, hypertension and diabetes mellitus. Experimental evidence: some well-designed RCTs found increased CVD risk factors with decreasing 25(OH)D levels. Analogy: the association between serum 25(OH)D levels and CVD risk is analogous to that between 25(OH)D levels and the risk of overall cancer, periodontal disease, multiple sclerosis and breast cancer. all relevant Hill criteria for a causal association in a biological system are satisfied to indicate a low 25(OH)D level as a CVD risk factor.

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.

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

Quantum correlations with no causal order

PubMed Central

Oreshkov, Ognyan; Costa, Fabio; Brukner, Časlav

2012-01-01

The idea that events obey a definite causal order is deeply rooted in our understanding of the world and at the basis of the very notion of time. But where does causal order come from, and is it a necessary property of nature? Here, we address these questions from the standpoint of quantum mechanics in a new framework for multipartite correlations that does not assume a pre-defined global causal structure but only the validity of quantum mechanics locally. All known situations that respect causal order, including space-like and time-like separated experiments, are captured by this framework in a unified way. Surprisingly, we find correlations that cannot be understood in terms of definite causal order. These correlations violate a 'causal inequality' that is satisfied by all space-like and time-like correlations. We further show that in a classical limit causal order always arises, which suggests that space-time may emerge from a more fundamental structure in a quantum-to-classical transition. PMID:23033068

Paradoxical Behavior of Granger Causality

NASA Astrophysics Data System (ADS)

Witt, Annette; Battaglia, Demian; Gail, Alexander

2013-03-01

Granger causality is a standard tool for the description of directed interaction of network components and is popular in many scientific fields including econometrics, neuroscience and climate science. For time series that can be modeled as bivariate auto-regressive processes we analytically derive an expression for spectrally decomposed Granger Causality (SDGC) and show that this quantity depends only on two out of four groups of model parameters. Then we present examples of such processes whose SDGC expose paradoxical behavior in the sense that causality is high for frequency ranges with low spectral power. For avoiding misinterpretations of Granger causality analysis we propose to complement it by partial spectral analysis. Our findings are illustrated by an example from brain electrophysiology. Finally, we draw implications for the conventional definition of Granger causality. Bernstein Center for Computational Neuroscience Goettingen

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

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

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.

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.

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…

Taste-related factors and food neophobia: Are they associated with nutritional status and teenagers' food choices?

PubMed

de Andrade Previato, Helena Dória Ribeiro; Behrens, Jorge Herman

2017-10-01

The aim of this study was to evaluate the association of taste-related factors (craving for sweets, using food as a reward and pleasure) and food neophobia with nutritional status and food intake among teenagers. This was a cross-sectional study with 132 teenagers 15 to 19 y of age. Food behavior, anthropometrics, body composition, and lifestyle measurements were obtained and analyzed. Craving for sweets was associated with overweight, adiposity, meal skipping, physical inactivity, and intake of sweets (P < 0.05). Reward was linked to adiposity, physical inactivity, lack of interest in information about food, and intake of sweets (P < 0.05). Pleasure was associated with physical inactivity, lack of interest in information about food, and intake of sweets and soft drinks (P < 0.05). Teenage girls had a higher craving for sweets (22.88 ± 4.77) and higher pleasure scores (21.50 ± 3.82), body fat (25.33 ± 6.60), meal skipping (63.2%), and physical inactivity (64.7%) than their male counterparts (P < 0.05). There was no association among food neophobia, nutritional status, and food intake. The results of the present study indicated that, in contrast to food neophobia, taste-related factors can be associated with body fat and inadequate food choices in teenagers. However, this was a cross-sectional study and further cohort studies should be performed for in-depth investigation of a causal relationship between the findings of this research. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

Investigating the multi-causal and complex nature of the accident causal influence of construction project features.

PubMed

Manu, Patrick A; Ankrah, Nii A; Proverbs, David G; Suresh, Subashini

2012-09-01

Construction project features (CPFs) are organisational, physical and operational attributes that characterise construction projects. Although previous studies have examined the accident causal influence of CPFs, the multi-causal attribute of this causal phenomenon still remain elusive and thus requires further investigation. Aiming to shed light on this facet of the accident causal phenomenon of CPFs, this study examines relevant literature and crystallises the attained insight of the multi-causal attribute by a graphical model which is subsequently operationalised by a derived mathematical risk expression that offers a systematic approach for evaluating the potential of CPFs to cause harm and consequently their health and safety (H&S) risk implications. The graphical model and the risk expression put forth by the study thus advance current understanding of the accident causal phenomenon of CPFs and they present an opportunity for project participants to manage the H&S risk associated with CPFs from the early stages of project procurement. Copyright © 2011 Elsevier Ltd. All rights reserved.

Towards graphical causal structures

NASA Astrophysics Data System (ADS)

Paulsson, K. Johan

2012-12-01

Folowing recent work by R. Spekkens, M. Leifer and B. Coecke we investigate causal settings in a limited categorical version of the conditional density operator formalism. We particularly show how the compact structure for causal and acausal settings apply on the measurements of stabiliser theory.

In the eye of the beholder: Can counter-stereotypes change perceptions of older adults' social status?

PubMed

Robertson, Deirdre A; Weiss, David

2017-09-01

Negative age-related stereotypes often entail the perception that older adults have a lower social status than middle-aged adults. We hypothesized that older adults are perceived to have lower social status because they are less likely to be seen in prestigious occupational positions. People tend to infer general assumptions about group characteristics from exemplars. According to this, presenting a stereotype-inconsistent exemplar (i.e., older person in a high-status position) should change perceptions of older adults' social status. Study 1 (60 countries, N = 86,026, 18-99 years) showed that people in countries with an older relative to a younger political leader do not perceive as great a decline in social status from middle-aged to older adults. Study 2 (N = 131; 19-74 years) tested the causal link demonstrating that participants exposed to older exemplars holding a prestigious occupational position were significantly more likely to rate older adults as having a relative higher social status. We discuss implications for future interventions to change negative age-related stereotypes. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

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.

Principal stratification in causal inference.

PubMed

Frangakis, Constantine E; Rubin, Donald B

2002-03-01

Many scientific problems require that treatment comparisons be adjusted for posttreatment variables, but the estimands underlying standard methods are not causal effects. To address this deficiency, we propose a general framework for comparing treatments adjusting for posttreatment variables that yields principal effects based on principal stratification. Principal stratification with respect to a posttreatment variable is a cross-classification of subjects defined by the joint potential values of that posttreatment variable tinder each of the treatments being compared. Principal effects are causal effects within a principal stratum. The key property of principal strata is that they are not affected by treatment assignment and therefore can be used just as any pretreatment covariate. such as age category. As a result, the central property of our principal effects is that they are always causal effects and do not suffer from the complications of standard posttreatment-adjusted estimands. We discuss briefly that such principal causal effects are the link between three recent applications with adjustment for posttreatment variables: (i) treatment noncompliance, (ii) missing outcomes (dropout) following treatment noncompliance. and (iii) censoring by death. We then attack the problem of surrogate or biomarker endpoints, where we show, using principal causal effects, that all current definitions of surrogacy, even when perfectly true, do not generally have the desired interpretation as causal effects of treatment on outcome. We go on to forrmulate estimands based on principal stratification and principal causal effects and show their superiority.

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…

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

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.

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

Does Sufficient Evidence Exist to Support a Causal Association between Vitamin D Status and Cardiovascular Disease Risk? An Assessment Using Hill’s Criteria for Causality

PubMed Central

Weyland, Patricia G.; Grant, William B.; Howie-Esquivel, Jill

2014-01-01

Serum 25-hydroxyvitamin D (25(OH)D) levels have been found to be inversely associated with both prevalent and incident cardiovascular disease (CVD) risk factors; dyslipidemia, hypertension and diabetes mellitus. This review looks for evidence of a causal association between low 25(OH)D levels and increased CVD risk. We evaluated journal articles in light of Hill’s criteria for causality in a biological system. The results of our assessment are as follows. Strength of association: many randomized controlled trials (RCTs), prospective and cross-sectional studies found statistically significant inverse associations between 25(OH)D levels and CVD risk factors. Consistency of observed association: most studies found statistically significant inverse associations between 25(OH)D levels and CVD risk factors in various populations, locations and circumstances. Temporality of association: many RCTs and prospective studies found statistically significant inverse associations between 25(OH)D levels and CVD risk factors. Biological gradient (dose-response curve): most studies assessing 25(OH)D levels and CVD risk found an inverse association exhibiting a linear biological gradient. Plausibility of biology: several plausible cellular-level causative mechanisms and biological pathways may lead from a low 25(OH)D level to increased risk for CVD with mediators, such as dyslipidemia, hypertension and diabetes mellitus. Experimental evidence: some well-designed RCTs found increased CVD risk factors with decreasing 25(OH)D levels. Analogy: the association between serum 25(OH)D levels and CVD risk is analogous to that between 25(OH)D levels and the risk of overall cancer, periodontal disease, multiple sclerosis and breast cancer. Conclusion: all relevant Hill criteria for a causal association in a biological system are satisfied to indicate a low 25(OH)D level as a CVD risk factor. PMID:25184368

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

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

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).

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.

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

Granger causality for state-space models

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Seth, Anil K.

2015-04-01

Granger causality has long been a prominent method for inferring causal interactions between stochastic variables for a broad range of complex physical systems. However, it has been recognized that a moving average (MA) component in the data presents a serious confound to Granger causal analysis, as routinely performed via autoregressive (AR) modeling. We solve this problem by demonstrating that Granger causality may be calculated simply and efficiently from the parameters of a state-space (SS) model. Since SS models are equivalent to autoregressive moving average models, Granger causality estimated in this fashion is not degraded by the presence of a MA component. This is of particular significance when the data has been filtered, downsampled, observed with noise, or is a subprocess of a higher dimensional process, since all of these operations—commonplace in application domains as diverse as climate science, econometrics, and the neurosciences—induce a MA component. We show how Granger causality, conditional and unconditional, in both time and frequency domains, may be calculated directly from SS model parameters via solution of a discrete algebraic Riccati equation. Numerical simulations demonstrate that Granger causality estimators thus derived have greater statistical power and smaller bias than AR estimators. We also discuss how the SS approach facilitates relaxation of the assumptions of linearity, stationarity, and homoscedasticity underlying current AR methods, thus opening up potentially significant new areas of research in Granger causal analysis.

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

Theory-Based Causal Induction

ERIC Educational Resources Information Center

Griffiths, Thomas L.; Tenenbaum, Joshua B.

2009-01-01

Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations…

Designing Effective Supports for Causal Reasoning

ERIC Educational Resources Information Center

Jonassen, David H.; Ionas, Ioan Gelu

2008-01-01

Causal reasoning represents one of the most basic and important cognitive processes that underpin all higher-order activities, such as conceptual understanding and problem solving. Hume called causality the "cement of the universe" [Hume (1739/2000). Causal reasoning is required for making predictions, drawing implications and…

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.

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

Spatial-temporal causal modeling: a data centric approach to climate change attribution (Invited)

NASA Astrophysics Data System (ADS)

Lozano, A. C.

2010-12-01

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.

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

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…

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.

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

Amodal causal capture in the tunnel effect.

PubMed

Bae, Gi Yeul; Flombaum, Jonathan I

2011-01-01

In addition to identifying individual objects in the world, the visual system must also characterize the relationships between objects, for instance when objects occlude one another or cause one another to move. Here we explored the relationship between perceived causality and occlusion. Can one perceive causality in an occluded location? In several experiments, observers judged whether a centrally presented event involved a single object passing behind an occluder, or one object causally launching another (out of view and behind the occluder). With no additional context, the centrally presented event was typically judged as a non-causal pass, even when the occluding and disoccluding objects were different colors--an illusion known as the 'tunnel effect' that results from spatiotemporal continuity. However, when a synchronized context event involved an unambiguous causal launch, participants perceived a causal launch behind the occluder. This percept of an occluded causal interaction could also be driven by grouping and synchrony cues in the absence of any explicitly causal interaction. These results reinforce the hypothesis that causality is an aspect of perception. It is among the interpretations of the world that are independently available to vision when resolving ambiguity, and that the visual system can 'fill in' amodally.

Exploring the relation between positive emotions and the functional status of older adults living independently: a systematic review.

PubMed

Cabrita, Miriam; Lamers, Sanne M A; Trompetter, Hester R; Tabak, Monique; Vollenbroek-Hutten, Miriam M R

2017-11-01

Literature suggests that positive emotions positively influence physiological parameters but their relation to functioning in the daily life of older adults living independently remains unclear. The present work aims to investigate the relation between positive emotions and functional status in daily life of older people living independently. A systematic literature review was conducted using the PubMed, PsycINFO and Scopus electronic databases. Included works were peer-reviewed empirical studies that analysed the relation between positive emotions and ability to perform activities of daily living with older adults living independently. After removal of duplicates, 10 out of 963 papers met the inclusion criteria. Cross-sectional studies (n = 6) provided limited evidence about a relation between positive emotions and functioning in daily life. However, longitudinal studies (n = 4) provide significant evidence for an interaction between the two factors, suggesting that time influences this interaction. The variety on the design and samples of the studies included in this review does not allow a cohesive conclusion of the results. Nevertheless, limited evidence suggests that higher frequency in the experience of positive emotions might be associated with lower functional limitations. The issue of causality in emotions-functioning remains unclear from the review. Further observational studies are highly recommended, supported by innovative technologies.

Expectations and Interpretations during Causal Learning

ERIC Educational Resources Information Center

Luhmann, Christian C.; Ahn, Woo-kyoung

2011-01-01

In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to…

Representing Personal Determinants in Causal Structures.

ERIC Educational Resources Information Center

Bandura, Albert

1984-01-01

Responds to Staddon's critique of the author's earlier article and addresses issues raised by Staddon's (1984) alternative models of causality. The author argues that it is not the formalizability of causal processes that is the issue but whether cognitive determinants of behavior are reducible to past stimulus inputs in causal structures.…

[Causal analysis approaches in epidemiology].

PubMed

Dumas, O; Siroux, V; Le Moual, N; Varraso, R

2014-02-01

Epidemiological research is mostly based on observational studies. Whether such studies can provide evidence of causation remains discussed. Several causal analysis methods have been developed in epidemiology. This paper aims at presenting an overview of these methods: graphical models, path analysis and its extensions, and models based on the counterfactual approach, with a special emphasis on marginal structural models. Graphical approaches have been developed to allow synthetic representations of supposed causal relationships in a given problem. They serve as qualitative support in the study of causal relationships. The sufficient-component cause model has been developed to deal with the issue of multicausality raised by the emergence of chronic multifactorial diseases. Directed acyclic graphs are mostly used as a visual tool to identify possible confounding sources in a study. Structural equations models, the main extension of path analysis, combine a system of equations and a path diagram, representing a set of possible causal relationships. They allow quantifying direct and indirect effects in a general model in which several relationships can be tested simultaneously. Dynamic path analysis further takes into account the role of time. The counterfactual approach defines causality by comparing the observed event and the counterfactual event (the event that would have been observed if, contrary to the fact, the subject had received a different exposure than the one he actually received). This theoretical approach has shown limits of traditional methods to address some causality questions. In particular, in longitudinal studies, when there is time-varying confounding, classical methods (regressions) may be biased. Marginal structural models have been developed to address this issue. In conclusion, "causal models", though they were developed partly independently, are based on equivalent logical foundations. A crucial step in the application of these models is the

Repeated Causal Decision Making

ERIC Educational Resources Information Center

Hagmayer, York; Meder, Bjorn

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…

Causality in Classical Electrodynamics

ERIC Educational Resources Information Center

Savage, Craig

2012-01-01

Causality in electrodynamics is a subject of some confusion, especially regarding the application of Faraday's law and the Ampere-Maxwell law. This has led to the suggestion that we should not teach students that electric and magnetic fields can cause each other, but rather focus on charges and currents as the causal agents. In this paper I argue…

"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.

Economic status and temperature-related mortality in Asia

NASA Astrophysics Data System (ADS)

Lim, Youn-Hee; Bell, Michelle L.; Kan, Haidong; Honda, Yasushi; Guo, Yue-Liang Leon; Kim, Ho

2015-10-01

In developed countries, low latitude and high temperature are positively associated with the population's ability to adapt to heat. However, few studies have examined the effect of economic status on the relationship between long-term exposure to high temperature and health. We compared heterogeneous temperature-related mortality effects relative to the average summer temperature in high-socioeconomic-status (SES) cities to temperature-related effects in low-SES cities. In the first stage of the research, we conducted a linear regression analysis to quantify the mortality effects of high temperature (at or above the 95th percentile) in 32 cities in Taiwan, China, Japan, and Korea. In the second stage, we used a meta-regression to examine the association between mortality risk with average summer temperature and gross domestic product (GDP) per capita. In cities with a low GDP per capita (less than 20,000 USD), the effects of temperature were detrimental to the population if the long-term average summer temperature was high. In contrast, in cities with a high GDP per capita, temperature-related mortality risk was not significantly related to average summer temperature. The relationship between long-term average summer temperature and the short-term effects of high temperatures differed based on the city-level economic status.

Kant on causal laws and powers.

PubMed

Henschen, Tobias

2014-12-01

The aim of the paper is threefold. Its first aim is to defend Eric Watkins's claim that for Kant, a cause is not an event but a causal power: a power that is borne by a substance, and that, when active, brings about its effect, i.e. a change of the states of another substance, by generating a continuous flow of intermediate states of that substance. The second aim of the paper is to argue against Watkins that the Kantian concept of causal power is not the pre-critical concept of real ground but the category of causality, and that Kant holds with Hume that causal laws cannot be inferred non-inductively (that he accordingly has no intention to show in the Second analogy or elsewhere that events fall under causal laws). The third aim of the paper is to compare the Kantian position on causality with central tenets of contemporary powers ontology: it argues that unlike the variants endorsed by contemporary powers theorists, the Kantian variants of these tenets are resistant to objections that neo-Humeans raise to these tenets.

Causality and Causal Inference in Social Work: Quantitative and Qualitative Perspectives

PubMed Central

Palinkas, Lawrence A.

2015-01-01

Achieving the goals of social work requires matching a specific solution to a specific problem. Understanding why the problem exists and why the solution should work requires a consideration of cause and effect. However, it is unclear whether it is desirable for social workers to identify cause and effect, whether it is possible for social workers to identify cause and effect, and, if so, what is the best means for doing so. These questions are central to determining the possibility of developing a science of social work and how we go about doing it. This article has four aims: (1) provide an overview of the nature of causality; (2) examine how causality is treated in social work research and practice; (3) highlight the role of quantitative and qualitative methods in the search for causality; and (4) demonstrate how both methods can be employed to support a “science” of social work. PMID:25821393

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).

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

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

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.

MIIC online: a web server to reconstruct causal or non-causal networks from non-perturbative data.

PubMed

Sella, Nadir; Verny, Louis; Uguzzoni, Guido; Affeldt, Séverine; Isambert, Hervé

2018-07-01

We present a web server running the MIIC algorithm, a network learning method combining constraint-based and information-theoretic frameworks to reconstruct causal, non-causal or mixed networks from non-perturbative data, without the need for an a priori choice on the class of reconstructed network. Starting from a fully connected network, the algorithm first removes dispensable edges by iteratively subtracting the most significant information contributions from indirect paths between each pair of variables. The remaining edges are then filtered based on their confidence assessment or oriented based on the signature of causality in observational data. MIIC online server can be used for a broad range of biological data, including possible unobserved (latent) variables, from single-cell gene expression data to protein sequence evolution and outperforms or matches state-of-the-art methods for either causal or non-causal network reconstruction. MIIC online can be freely accessed at https://miic.curie.fr. Supplementary data are available at Bioinformatics online.

22 CFR 42.42 - Petitions for immediate relative or preference status.

Code of Federal Regulations, 2012 CFR

2012-04-01

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22 CFR 42.42 - Petitions for immediate relative or preference status.

Code of Federal Regulations, 2013 CFR

2013-04-01

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22 CFR 42.42 - Petitions for immediate relative or preference status.

Code of Federal Regulations, 2014 CFR

2014-04-01

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22 CFR 42.42 - Petitions for immediate relative or preference status.

Code of Federal Regulations, 2011 CFR

2011-04-01

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22 CFR 42.42 - Petitions for immediate relative or preference status.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Petitions for immediate relative or preference status. 42.42 Section 42.42 Foreign Relations DEPARTMENT OF STATE VISAS VISAS: DOCUMENTATION OF... may not issue a visa to an alien as an immediate relative entitled to status under 201(b), a family...

Causal inference in economics and marketing.

PubMed

Varian, Hal R

2016-07-05

This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual-a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.

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

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.

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

Does Causality Matter More Now? Increase in the Proportion of Causal Language in English Texts.

PubMed

Iliev, Rumen; Axelrod, Robert

2016-05-01

The vast majority of the work on culture and cognition has focused on cross-cultural comparisons, largely ignoring the dynamic aspects of culture. In this article, we provide a diachronic analysis of causal cognition over time. We hypothesized that the increased role of education, science, and technology in Western societies should be accompanied by greater attention to causal connections. To test this hypothesis, we compared word frequencies in English texts from different time periods and found an increase in the use of causal language of about 40% over the past two centuries. The observed increase was not attributable to general language effects or to changing semantics of causal words. We also found that there was a consistent difference between the 19th and the 20th centuries, and that the increase happened mainly in the 20th century. © The Author(s) 2016.

Causal inference, probability theory, and graphical insights.

PubMed

Baker, Stuart G

2013-11-10

Causal inference from observational studies is a fundamental topic in biostatistics. The causal graph literature typically views probability theory as insufficient to express causal concepts in observational studies. In contrast, the view here is that probability theory is a desirable and sufficient basis for many topics in causal inference for the following two reasons. First, probability theory is generally more flexible than causal graphs: Besides explaining such causal graph topics as M-bias (adjusting for a collider) and bias amplification and attenuation (when adjusting for instrumental variable), probability theory is also the foundation of the paired availability design for historical controls, which does not fit into a causal graph framework. Second, probability theory is the basis for insightful graphical displays including the BK-Plot for understanding Simpson's paradox with a binary confounder, the BK2-Plot for understanding bias amplification and attenuation in the presence of an unobserved binary confounder, and the PAD-Plot for understanding the principal stratification component of the paired availability design. Published 2013. This article is a US Government work and is in the public domain in the USA.

Generalized Causal Quantum Theories

NASA Astrophysics Data System (ADS)

Parmeggiani, Claudio

2007-12-01

We shall show that is always possible to construct causal Quantum Theories fully equivalent (as predictive tools) to acausal, standard Quantum Theory, relativistic or not relativistic; we re-obtain, as a particular case, the usual Quantum Bohmian Theory. Then we consider the measurement process, in causal theories, and we conclude that the state of affairs is not really improved, with respect to standard theories.

Causal inference in economics and marketing

PubMed Central

Varian, Hal R.

2016-01-01

This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual—a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference. PMID:27382144

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.

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.

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.

Bayesian networks improve causal environmental ...

EPA Pesticide Factsheets

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value

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

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

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.

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.

Expert Causal Reasoning and Explanation.

ERIC Educational Resources Information Center

Kuipers, Benjamin

The relationship between cognitive psychologists and researchers in artificial intelligence carries substantial benefits for both. An ongoing investigation in causal reasoning in medical problem solving systems illustrates this interaction. This paper traces a dialectic of sorts in which three different types of causal resaoning for medical…

Causal Inference in Retrospective Studies.

ERIC Educational Resources Information Center

Holland, Paul W.; Rubin, Donald B.

1988-01-01

The problem of drawing causal inferences from retrospective case-controlled studies is considered. A model for causal inference in prospective studies is applied to retrospective studies. Limitations of case-controlled studies are formulated concerning relevant parameters that can be estimated in such studies. A coffee-drinking/myocardial…

Causal Learning with Local Computations

ERIC Educational Resources Information Center

Fernbach, Philip M.; Sloman, Steven A.

2009-01-01

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…

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.

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

White rot of garlic and onion (Causal agent, Sclerotium cepivorum): A status report from the Pacific Northwest

USDA-ARS?s Scientific Manuscript database

There is evidence from literature, state department of agriculture documents, and recent diagnoses that Sclerotium cepivorum, causal agent of white rot of garlic and onion, is spreading and/or becoming more established in the Pacific Northwest. Previously documented distributions are summarized, an...

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

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

Non-Gaussian Methods for Causal Structure Learning.

PubMed

Shimizu, Shohei

2018-05-22

Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.

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…

Evaluation of quality of life related to nutritional status.

PubMed

Wanden-Berghe, Carmina; Sanz-Valero, Javier; Escribà-Agüir, Vicenta; Castelló-Botia, Isabel; Guardiola-Wanden-Berghe, Rocio

2009-04-01

The way in which the quality of life related to health (HRQoL) is affected by the nutritional status of the patient is a subject of constant interest and permanent debate. The purpose of the present paper is to review those studies that relate HRQoL to nutritional status and examine the tools (questionnaires) that they use to investigate this relationship. A critical review of published studies was carried out via an investigation of the following databases: MEDLINE (via PubMed); EMBASE; The Cochrane Library; Cumulative Index to Nursing and Allied Health Literature (CINAHL); Institute for Scientific Information (ISI) Web of Science; Latin American and Caribbean Health Sciences Literature (LILACS); Spanish Health Sciences Bibliographic Index (IBECS). The search was carried out from the earliest date possible until July 2007.The medical subject heading terms used were 'quality of life', 'nutritional status' and 'questionnaires'. The articles had to contain at least one questionnaire that evaluated quality of life. Twenty-eight documents fulfilling the inclusion criteria were accepted, although none of them used a specific questionnaire to evaluate HRQoL related to nutritional status. However, some of them used a combination of generic questionnaires with the intention of evaluating the same. Only three studies selectively addressed the relationship between nutritional status and quality of life, this evaluation being performed not by means of specific questionnaires but by statistical analysis of data obtained via validated questionnaires.

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…

Exploring Individual Differences in Preschoolers' Causal Stance

ERIC Educational Resources Information Center

Alvarez, Aubry; Booth, Amy E.

2016-01-01

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…

Constraints on Children's Judgments of Magical Causality

ERIC Educational Resources Information Center

Woolley, Jacqueline D.; Browne, Cheryl A.; Boerger, Elizabeth A.

2006-01-01

In 3 studies we addressed the operation of constraints on children's causal judgments. Our primary focus was whether children's beliefs about magical causality, specifically wishing, are constrained by features that govern the attribution of ordinary causality. In Experiment 1, children witnessed situations in which a confederate's wish appeared…

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

Granger-causality maps of diffusion processes.

PubMed

Wahl, Benjamin; Feudel, Ulrike; Hlinka, Jaroslav; Wächter, Matthias; Peinke, Joachim; Freund, Jan A

2016-02-01

Granger causality is a statistical concept devised to reconstruct and quantify predictive information flow between stochastic processes. Although the general concept can be formulated model-free it is often considered in the framework of linear stochastic processes. Here we show how local linear model descriptions can be employed to extend Granger causality into the realm of nonlinear systems. This novel treatment results in maps that resolve Granger causality in regions of state space. Through examples we provide a proof of concept and illustrate the utility of these maps. Moreover, by integration we convert the local Granger causality into a global measure that yields a consistent picture for a global Ornstein-Uhlenbeck process. Finally, we recover invariance transformations known from the theory of autoregressive processes.

The role of physical formidability in human social status allocation.

PubMed

Lukaszewski, Aaron W; Simmons, Zachary L; Anderson, Cameron; Roney, James R

2016-03-01

Why are physically formidable men willingly allocated higher social status by others in cooperative groups? Ancestrally, physically formidable males would have been differentially equipped to generate benefits for groups by providing leadership services of within-group enforcement (e.g., implementing punishment of free riders) and between-group representation (e.g., negotiating with other coalitions). Therefore, we hypothesize that adaptations for social status allocation are designed to interpret men's physical formidability as a cue to these leadership abilities, and to allocate greater status to formidable men on this basis. These hypotheses were supported in 4 empirical studies wherein young adults rated standardized photos of subjects (targets) who were described as being part of a white-collar business consultancy. In Studies 1 and 2, male targets' physical strength positively predicted ratings of their projected status within the organization, and this effect was mediated by perceptions that stronger men possessed greater leadership abilities of within-group enforcement and between-group representation. Moreover, (a) these same patterns held whether status was conceptualized as overall ascendancy, prestige-based status, or dominance-based status, and (b) strong men who were perceived as aggressively self-interested were not allocated greater status. Finally, 2 experiments established the causality of physical formidability's effects on status-related perceptions by manipulating targets' relative strength (Study 3) and height (Study 4). In interpreting our findings, we argue that adaptations for formidability-based status allocation may have facilitated the evolution of group cooperation in humans and other primates. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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.

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%.

Causal Inference and Developmental Psychology

ERIC Educational Resources Information Center

Foster, E. Michael

2010-01-01

Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether…

Evaluating the Impact of Parent-Reported Medical Home Status on Children's Health Care Utilization, Expenditures, and Quality: A Difference-in-Differences Analysis with Causal Inference Methods.

PubMed

Han, Bing; Yu, Hao; Friedberg, Mark W

2017-04-01

To evaluate the effects of the parent-reported medical home status on health care utilization, expenditures, and quality for children. Medical Expenditure Panel Survey (MEPS) during 2004-2012, including a total of 9,153 children who were followed up for 2 years in the survey. We took a causal difference-in-differences approach using inverse probability weighting and doubly robust estimators to study how changes in medical home status over a 2-year period affected children's health care outcomes. Our analysis adjusted for children's sociodemographic, health, and insurance statuses. We conducted sensitivity analyses using alternative statistical methods, different approaches to outliers and missing data, and accounting for possible common-method biases. Compared with children whose parents reported having medical homes in both years 1 and 2, those who had medical homes in year 1 but lost them in year 2 had significantly lower parent-reported ratings of health care quality and higher utilization of emergency care. Compared with children whose parents reported having no medical homes in both years, those who did not have medical homes in year 1 but gained them in year 2 had significantly higher ratings of health care quality, but no significant differences in health care expenditures and utilization. Having a medical home may help improve health care quality for children; losing a medical home may lead to higher utilization of emergency care. © Health Research and Educational Trust.

Developing Causal Understanding with Causal Maps: The Impact of Total Links, Temporal Flow, and Lateral Position of Outcome Nodes

ERIC Educational Resources Information Center

Jeong, Allan; Lee, Woon Jee

2012-01-01

This study examined some of the methodological approaches used by students to construct causal maps in order to determine which approaches help students understand the underlying causes and causal mechanisms in a complex system. This study tested the relationship between causal understanding (ratio of root causes correctly/incorrectly identified,…

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.

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.

Multiple Causality: Consequences for Medical Practice

PubMed Central

Nydegger, Corinne N.

1983-01-01

When a scientifically trained health professional is called upon to deal with patients holding differing causal views of illness, the resulting lack of communication is frustrating to both. This discussion traces some implications for medical practice of significant cultural differences in two aspects of causal paradigms of illness: (1) terms accepted and (2) dimension or level of causality typically sought. The second is the more pervasive and intractable problem, having distinctive consequences for the role of curer, symptomatology, diagnosis and treatment. PMID:6858133

Causal assessment of dietary acid load and bone disease: a systematic review & meta-analysis applying Hill's epidemiologic criteria for causality

PubMed Central

2011-01-01

Background Modern diets have been suggested to increase systemic acid load and net acid excretion. In response, alkaline diets and products are marketed to avoid or counteract this acid, help the body regulate its pH to prevent and cure disease. The objective of this systematic review was to evaluate causal relationships between dietary acid load and osteoporosis using Hill's criteria. Methods Systematic review and meta-analysis. We systematically searched published literature for randomized intervention trials, prospective cohort studies, and meta-analyses of the acid-ash or acid-base diet hypothesis with bone-related outcomes, in which the diet acid load was altered, or an alkaline diet or alkaline salts were provided, to healthy human adults. Cellular mechanism studies were also systematically examined. Results Fifty-five of 238 studies met the inclusion criteria: 22 randomized interventions, 2 meta-analyses, and 11 prospective observational studies of bone health outcomes including: urine calcium excretion, calcium balance or retention, changes of bone mineral density, or fractures, among healthy adults in which acid and/or alkaline intakes were manipulated or observed through foods or supplements; and 19 in vitro cell studies which examined the hypothesized mechanism. Urine calcium excretion rates were consistent with osteoporosis development; however calcium balance studies did not demonstrate loss of whole body calcium with higher net acid excretion. Several weaknesses regarding the acid-ash hypothesis were uncovered: No intervention studies provided direct evidence of osteoporosis progression (fragility fractures, or bone strength as measured using biopsy). The supporting prospective cohort studies were not controlled regarding important osteoporosis risk factors including: weight loss during follow-up, family history of osteoporosis, baseline bone mineral density, and estrogen status. No study revealed a biologic mechanism functioning at physiological p

Causal assessment of dietary acid load and bone disease: a systematic review & meta-analysis applying Hill's epidemiologic criteria for causality.

PubMed

Fenton, Tanis R; Tough, Suzanne C; Lyon, Andrew W; Eliasziw, Misha; Hanley, David A

2011-04-30

Modern diets have been suggested to increase systemic acid load and net acid excretion. In response, alkaline diets and products are marketed to avoid or counteract this acid, help the body regulate its pH to prevent and cure disease. The objective of this systematic review was to evaluate causal relationships between dietary acid load and osteoporosis using Hill's criteria. Systematic review and meta-analysis. We systematically searched published literature for randomized intervention trials, prospective cohort studies, and meta-analyses of the acid-ash or acid-base diet hypothesis with bone-related outcomes, in which the diet acid load was altered, or an alkaline diet or alkaline salts were provided, to healthy human adults. Cellular mechanism studies were also systematically examined. Fifty-five of 238 studies met the inclusion criteria: 22 randomized interventions, 2 meta-analyses, and 11 prospective observational studies of bone health outcomes including: urine calcium excretion, calcium balance or retention, changes of bone mineral density, or fractures, among healthy adults in which acid and/or alkaline intakes were manipulated or observed through foods or supplements; and 19 in vitro cell studies which examined the hypothesized mechanism. Urine calcium excretion rates were consistent with osteoporosis development; however calcium balance studies did not demonstrate loss of whole body calcium with higher net acid excretion. Several weaknesses regarding the acid-ash hypothesis were uncovered: No intervention studies provided direct evidence of osteoporosis progression (fragility fractures, or bone strength as measured using biopsy). The supporting prospective cohort studies were not controlled regarding important osteoporosis risk factors including: weight loss during follow-up, family history of osteoporosis, baseline bone mineral density, and estrogen status. No study revealed a biologic mechanism functioning at physiological pH. Finally, randomized studies

Identity, causality, and pronoun ambiguity.

PubMed

Sagi, Eyal; Rips, Lance J

2014-10-01

This article looks at the way people determine the antecedent of a pronoun in sentence pairs, such as: Albert invited Ron to dinner. He spent hours cleaning the house. The experiment reported here is motivated by the idea that such judgments depend on reasoning about identity (e.g., the identity of the he who cleaned the house). Because the identity of an individual over time depends on the causal-historical path connecting the stages of the individual, the correct antecedent will also depend on causal connections. The experiment varied how likely it is that the event of the first sentence (e.g., the invitation) would cause the event of the second (the house cleaning) for each of the two individuals (the likelihood that if Albert invited Ron to dinner, this would cause Albert to clean the house, versus cause Ron to clean the house). Decisions about the antecedent followed causal likelihood. A mathematical model of causal identity accounted for most of the key aspects of the data from the individual sentence pairs. Copyright © 2014 Cognitive Science Society, Inc.

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

Causal reasoning with mental models

PubMed Central

Khemlani, Sangeet S.; Barbey, Aron K.; Johnson-Laird, Philip N.

2014-01-01

This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex. PMID:25389398

Causal reasoning with mental models.

PubMed

Khemlani, Sangeet S; Barbey, Aron K; Johnson-Laird, Philip N

2014-01-01

This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.

Wormholes, baby universes, and causality

NASA Astrophysics Data System (ADS)

Visser, Matt

1990-02-01

In this paper wormholes defined on a Minkowski signature manifold are considered, both at the classical and quantum levels. It is argued that causality in quantum gravity may best be imposed by restricting the functional integral to include only causal Lorentzian spacetimes. Subject to this assumption, one can put very tight constraints on the quantum behavior of wormholes, their cousins the baby universes, and topology-changing processes in general. Even though topology-changing processes are tightly constrained, this still allows very interesting geometrical (rather than topological) effects. In particular, the laboratory construction of baby universes is not prohibited provided that the ``umbilical cord'' is never cut. Methods for relaxing these causality constraints are also discussed.

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

A self-agency bias in preschoolers' causal inferences

PubMed Central

Kushnir, Tamar; Wellman, Henry M.; Gelman, Susan A.

2013-01-01

Preschoolers' causal learning from intentional actions – causal interventions – is subject to a self-agency bias. We propose that this bias is evidence-based; it is responsive to causal uncertainty. In the current studies, two causes (one child-controlled, one experimenter-controlled) were associated with one or two effects, first independently, then simultaneously. When initial independent effects were probabilistic, and thus subsequent simultaneous actions were causally ambiguous, children showed a self-agency bias. Children showed no bias when initial effects were deterministic. Further controls establish that children's self-agency bias is not a wholesale preference but rather is influenced by uncertainty in causal evidence. These results demonstrate that children's own experience of action influences their causal learning, and suggest possible benefits in uncertain and ambiguous everyday learning contexts. PMID:19271843

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'.

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…

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 Set Phenomenology

NASA Astrophysics Data System (ADS)

Philpott, Lydia

2010-09-01

Central to the development of any new theory is the investigation of the observable consequences of the theory. In the search for quantum gravity, research in phenomenology has been dominated by models violating Lorentz invariance (LI) -- despite there being, at present, no evidence that LI is violated. Causal set theory is a LI candidate theory of QG that seeks not to quantise gravity as such, but rather to develop a new understanding of the universe from which both GR and QM could arise separately. The key hypothesis is that spacetime is a discrete partial order: a set of events where the partial ordering is the physical causal ordering between the events. This thesis investigates Lorentz invariant QG phenomenology motivated by the causal set approach. Massive particles propagating in a discrete spacetime will experience diffusion in both position and momentum in proper time. This thesis considers this idea in more depth, providing a rigorous derivation of the diffusion equation in terms of observable cosmic time. The diffusion behaviour does not depend on any particular underlying particle model. Simulations of three different models are conducted, revealing behaviour that matches the diffusion equation despite limitations on the size of causal set simulated. The effect of spacetime discreteness on the behaviour of massless particles is also investigated. Diffusion equations in both affine time and cosmic time are derived, and it is found that massless particles undergo diffusion and drift in energy. Constraints are placed on the magnitudes of the drift and diffusion parameters by considering the blackbody nature of the CMB. Spacetime discreteness also has a potentially observable effect on photon polarisation. For linearly polarised photons, underlying discreteness is found to cause a rotation in polarisation angle and a suppression in overall polarisation.

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

Entanglement entropy in causal set theory

NASA Astrophysics Data System (ADS)

Sorkin, Rafael D.; Yazdi, Yasaman K.

2018-04-01

Entanglement entropy is now widely accepted as having deep connections with quantum gravity. It is therefore desirable to understand it in the context of causal sets, especially since they provide in a natural manner the UV cutoff needed to render entanglement entropy finite. Formulating a notion of entanglement entropy in a causal set is not straightforward because the type of canonical hypersurface-data on which its definition typically relies is not available. Instead, we appeal to the more global expression given in Sorkin (2012 (arXiv:1205.2953)) which, for a Gaussian scalar field, expresses the entropy of a spacetime region in terms of the field’s correlation function within that region (its ‘Wightman function’ W(x, x') ). Carrying this formula over to the causal set, one obtains an entropy which is both finite and of a Lorentz invariant nature. We evaluate this global entropy-expression numerically for certain regions (primarily order-intervals or ‘causal diamonds’) within causal sets of 1  +  1 dimensions. For the causal-set counterpart of the entanglement entropy, we obtain, in the first instance, a result that follows a (spacetime) volume law instead of the expected (spatial) area law. We find, however, that one obtains an area law if one truncates the commutator function (‘Pauli–Jordan operator’) and the Wightman function by projecting out the eigenmodes of the Pauli–Jordan operator whose eigenvalues are too close to zero according to a geometrical criterion which we describe more fully below. In connection with these results and the questions they raise, we also study the ‘entropy of coarse-graining’ generated by thinning out the causal set, and we compare it with what one obtains by similarly thinning out a chain of harmonic oscillators, finding the same, ‘universal’ behaviour in both cases.

Causal premise semantics.

PubMed

Kaufmann, Stefan

2013-08-01

The rise of causality and the attendant graph-theoretic modeling tools in the study of counterfactual reasoning has had resounding effects in many areas of cognitive science, but it has thus far not permeated the mainstream in linguistic theory to a comparable degree. In this study I show that a version of the predominant framework for the formal semantic analysis of conditionals, Kratzer-style premise semantics, allows for a straightforward implementation of the crucial ideas and insights of Pearl-style causal networks. I spell out the details of such an implementation, focusing especially on the notions of intervention on a network and backtracking interpretations of counterfactuals. Copyright © 2013 Cognitive Science Society, Inc.

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

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.

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.

Updating during reading comprehension: why causality matters.

PubMed

Kendeou, Panayiota; Smith, Emily R; O'Brien, Edward J

2013-05-01

The present set of 7 experiments systematically examined the effectiveness of adding causal explanations to simple refutations in reducing or eliminating the impact of outdated information on subsequent comprehension. The addition of a single causal-explanation sentence to a refutation was sufficient to eliminate any measurable disruption in comprehension caused by the outdated information (Experiment 1) but was not sufficient to eliminate its reactivation (Experiment 2). However, a 3 sentence causal-explanation addition to a refutation eliminated both any measurable disruption in comprehension (Experiment 3) and the reactivation of the outdated information (Experiment 4). A direct comparison between the 1 and 3 causal-explanation conditions provided converging evidence for these findings (Experiment 5). Furthermore, a comparison of the 3 sentence causal-explanation condition with a 3 sentence qualified-elaboration condition demonstrated that even though both conditions were sufficient to eliminate any measurable disruption in comprehension (Experiment 6), only the causal-explanation condition was sufficient to eliminate the reactivation of the outdated information (Experiment 7). These results establish a boundary condition under which outdated information will influence comprehension; they also have broader implications for both the updating process and knowledge revision in general.

Tools for Detecting Causality in Space Systems

NASA Astrophysics Data System (ADS)

Johnson, J.; Wing, S.

2017-12-01

Complex systems such as the solar and magnetospheric envivonment often exhibit patterns of behavior that suggest underlying organizing principles. Causality is a key organizing principle that is particularly difficult to establish in strongly coupled nonlinear systems, but essential for understanding and modeling the behavior of systems. While traditional methods of time-series analysis can identify linear correlations, they do not adequately quantify the distinction between causal and coincidental dependence. We discuss tools for detecting causality including: granger causality, transfer entropy, conditional redundancy, and convergent cross maps. The tools are illustrated by applications to magnetospheric and solar physics including radiation belt, Dst (a magnetospheric state variable), substorm, and solar cycle dynamics.

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…

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

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

Potential Diagnostic and Prognostic Value of Lymphocytic Mitochondrial DNA Deletion in Relation to Folic Acid Status in HCV-Related Hepatocellular Carcinoma

PubMed Central

Zekri, Abdel Rahman N; Salama, Hosny; Medhat, Eman; Hamdy, Sherif; Hassan, Zeinab K; Bakr, Yasser Mabrouk; Youssef, Amira Salah El - Din; Saleh, Doaa; Saeed, Ramy; Omran, Dalia

2017-01-01

Objective: We assessed the possibility of using mitochondrial (mt) DNA deletion as a molecular biomarker for disease progression in HCV-related hepatocellular carcinoma (HCC) and to identify its association with folic acid status. Methods: Serum folic acid and lymphocytic mtDNA deletions were assessed in 90 patients; 50 with HCC, 20 with liver cirrhosis (LC), and 20 with chronic hepatitis C (CHC) compared to 10 healthy control subjects. The diagnostic accuracy of mtDNA deletions frequency was evaluated using receiver-operating characteristic (ROC) curve analysis Survival analysis was performed using the Kaplan-Meier method. Differences in the survival rates were compared using log-rank test. Result: Our data revealed a significant elevation of mtDNA deletions frequency in the HCC group compared to the other groups (P-value <0.01). Also, our data showed a significant correlation between folate deficiency and high frequency of mtDNA deletions in patients with HCV-related HCC when compared to the other groups (r= -0.094 and P-value <0.05). Moreover, the size of the hepatic focal lesion in the HCC patients was positively correlated with mtDNA deletions (r= 0.09 and P-value <0.01). The median survival time for the HCC patients with high frequency of mtDNA deletions (∆Ct ≥3.9; 5.7+ 0.6 months) was significantly shorter than those with low mtDNA deletions frequency (∆Ct < 3.9; 11.9+ 0.04 months, P-value <0.01). Conclusion: Our data provided an evidence that lymphocytic mtDNA deletion could be used as non-invasive biomarker for disease progression and patients’ survival in HCV-related HCC. Also, our findings implied a causal relationship between the folate deficiency and the high mtDNA deletions frequency among Egyptian patients with HCV related HCC. PMID:28952275

Quasi-Experimental Designs for Causal Inference

ERIC Educational Resources Information Center

Kim, Yongnam; Steiner, Peter

2016-01-01

When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

Interpretational Confounding or Confounded Interpretations of Causal Indicators?

ERIC Educational Resources Information Center

Bainter, Sierra A.; Bollen, Kenneth A.

2014-01-01

In measurement theory, causal indicators are controversial and little understood. Methodological disagreement concerning causal indicators has centered on the question of whether causal indicators are inherently sensitive to interpretational confounding, which occurs when the empirical meaning of a latent construct departs from the meaning…

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

How multiple causes combine: independence constraints on causal inference.

PubMed

Liljeholm, Mimi

2015-01-01

According to the causal power view, two core constraints-that causes occur independently (i.e., no confounding) and influence their effects independently-serve as boundary conditions for causal induction. This study investigated how violations of these constraints modulate uncertainty about the existence and strength of a causal relationship. Participants were presented with pairs of candidate causes that were either confounded or not, and that either interacted or exerted their influences independently. Consistent with the causal power view, uncertainty about the existence and strength of causal relationships was greater when causes were confounded or interacted than when unconfounded and acting independently. An elemental Bayesian causal model captured differences in uncertainty due to confounding but not those due to an interaction. Implications of distinct sources of uncertainty for the selection of contingency information and causal generalization are discussed.

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.

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.

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.

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.

The role of counterfactual theory in causal reasoning.

PubMed

Maldonado, George

2016-10-01

In this commentary I review the fundamentals of counterfactual theory and its role in causal reasoning in epidemiology. I consider if counterfactual theory dictates that causal questions must be framed in terms of well-defined interventions. I conclude that it does not. I hypothesize that the interventionist approach to causal inference in epidemiology stems from elevating the randomized trial design to the gold standard for thinking about causal inference. I suggest that instead the gold standard we should use for thinking about causal inference in epidemiology is the thought experiment that, for example, compares an actual disease frequency under one exposure level with a counterfactual disease frequency under a different exposure level (as discussed in Greenland and Robins (1986) and Maldonado and Greenland (2002)). I also remind us that no method should be termed "causal" unless it addresses the effect of other biases in addition to the problem of confounding. Copyright © 2016 Elsevier Inc. All rights reserved.

The Effects of Experimentally Manipulated Social Status on Acute Eating Behavior: A Randomized, Crossover Pilot Study

PubMed Central

Cardel, MI; Johnson, SL; Beck, J; Dhurandhar, E; Keita, AD; Tomczik, AC; Pavela, G; Huo, T; Janicke, DM; Muller, K; Piff, PK; Peters, JC; Hill, JO; Allison, DB

2016-01-01

Both subjective and objectively measured social status has been associated with multiple health outcomes, including weight status, but the mechanism for this relationship remains unclear. Experimental studies may help identify the causal mechanisms underlying low social standing as a pathway for obesity. Our objective was to investigate the effects of experimentally manipulated social status on ad libitum acute dietary intakes and stress-related outcomes as potential mechanisms relating social status and weight. This was a pilot feasibility, randomized, crossover study in Hispanic young adults (n=9; age 19–25; 67% female; BMI ≥18.5 and ≤30 kg/m2). At visit 1, participants consumed a standardized breakfast and were randomized to a high social status position (HIGH) or low social status position (LOW) in a rigged game of Monopoly™. The rules for the game differed substantially in terms of degree of ‘privilege’ depending on randomization to HIGH or LOW. Following Monopoly™, participants were given an ad libitum buffet meal and energy intakes (kcal) were estimated by pre- and post-weighing foods consumed. Stress-related markers were measured at baseline, after the game of Monopoly™, and after lunch. Visit 2 used the same standardized protocol; however, participants were exposed to the opposite social status condition. When compared to HIGH, participants in LOW consumed 130 more calories (p=0.07) and a significantly higher proportion of their daily calorie needs in the ad libitum buffet meal (39% in LOW versus 31% in HIGH; p=0.04). In LOW, participants reported decreased feelings of pride and powerfulness following Monopoly™ (p=0.05) and after their lunch meal (p=0.08). Relative to HIGH, participants in LOW demonstrated higher heart rates following Monopoly™ (p=0.06), but this relationship was not significant once lunch was consumed (p=0.31). Our pilot data suggest a possible causal relationship between experimentally manipulated low social status

The effects of experimentally manipulated social status on acute eating behavior: A randomized, crossover pilot study.

PubMed

Cardel, M I; Johnson, S L; Beck, J; Dhurandhar, E; Keita, A D; Tomczik, A C; Pavela, G; Huo, T; Janicke, D M; Muller, K; Piff, P K; Peters, J C; Hill, J O; Allison, D B

2016-08-01

Both subjective and objectively measured social status has been associated with multiple health outcomes, including weight status, but the mechanism for this relationship remains unclear. Experimental studies may help identify the causal mechanisms underlying low social standing as a pathway for obesity. Our objective was to investigate the effects of experimentally manipulated social status on ad libitum acute dietary intakes and stress-related outcomes as potential mechanisms relating social status and weight. This was a pilot feasibility, randomized, crossover study in Hispanic young adults (n=9; age 19-25; 67% female; BMI ≥18.5 and ≤30kg/m(2)). At visit 1, participants consumed a standardized breakfast and were randomized to a high social status position (HIGH) or low social status position (LOW) in a rigged game of Monopoly™. The rules for the game differed substantially in terms of degree of 'privilege' depending on randomization to HIGH or LOW. Following Monopoly™, participants were given an ad libitum buffet meal and energy intakes (kcal) were estimated by pre- and post-weighing foods consumed. Stress-related markers were measured at baseline, after the game of Monopoly™, and after lunch. Visit 2 used the same standardized protocol; however, participants were exposed to the opposite social status condition. When compared to HIGH, participants in LOW consumed 130 more calories (p=0.07) and a significantly higher proportion of their daily calorie needs in the ad libitum buffet meal (39% in LOW versus 31% in HIGH; p=0.04). In LOW, participants reported decreased feelings of pride and powerfulness following Monopoly™ (p=0.05) and after their lunch meal (p=0.08). Relative to HIGH, participants in LOW demonstrated higher heart rates following Monopoly™ (p=0.06), but this relationship was not significant once lunch was consumed (p=0.31). Our pilot data suggest a possible causal relationship between experimentally manipulated low social status and

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

Subjective spacetime derived from a causal histories approach

NASA Astrophysics Data System (ADS)

Gunji, Yukio-Pegio; Haruna, Taichi; Uragami, Daisuke; Nishikawa, Asaki

2009-10-01

The internal description of spacetime can reveal ambiguity regarding an observer’s perception of the present, where an observer can refer to the present as if he were outside spacetime while actually existing in the present. This ambiguity can be expressed as the compatibility between an element and a set, and is here called a/{a}-compatibility. We describe a causal set as a lattice and a causal history as a quotient lattice, and implement the a/{a}-compatibility in the framework of a causal histories approach. This leads to a perpetual change of a pair of causal set and causal history, and can be used to describe subjective spacetime including the déjà vu experience and/or schizophrenic time.

A causal viscous cosmology without singularities

NASA Astrophysics Data System (ADS)

Laciana, Carlos E.

2017-05-01

An isotropic and homogeneous cosmological model with a source of dark energy is studied. That source is simulated with a viscous relativistic fluid with minimal causal correction. In this model the restrictions on the parameters coming from the following conditions are analized: (a) energy density without singularities along time, (b) scale factor increasing with time, (c) universe accelerated at present time, (d) state equation for dark energy with " w" bounded and close to -1. It is found that those conditions are satisfied for the following two cases. (i) When the transport coefficient (τ _{Π}), associated to the causal correction, is negative, with the additional restriction ζ | τ _{Π}| >2/3, where ζ is the relativistic bulk viscosity coefficient. The state equation is in the "phantom" energy sector. (ii) For τ _{Π} positive, in the "k-essence" sector. It is performed an exact calculation for the case where the equation of state is constant, finding that option (ii) is favored in relation to (i), because in (ii) the entropy is always increasing, while this does no happen in (i).

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.

Is High-Density Lipoprotein Cholesterol Causally Related to Kidney Function? Evidence From Genetic Epidemiological Studies.

PubMed

Coassin, Stefan; Friedel, Salome; Köttgen, Anna; Lamina, Claudia; Kronenberg, Florian

2016-11-01

A recent observational study with almost 2 million men reported an association between low high-density lipoprotein (HDL) cholesterol and worse kidney function. The causality of this association would be strongly supported if genetic variants associated with HDL cholesterol were also associated with kidney function. We used 68 genetic variants (single-nucleotide polymorphisms [SNPs]) associated with HDL cholesterol in genome-wide association studies including >188 000 subjects and tested their association with estimated glomerular filtration rate (eGFR) using summary statistics from another genome-wide association studies meta-analysis of kidney function including ≤133 413 subjects. Fourteen of the 68 SNPs (21%) had a P value <0.05 compared with the 5% expected by chance (Binomial test P=5.8×10 - 6 ). After Bonferroni correction, 6 SNPs were still significantly associated with eGFR. The genetic variants with the strongest associations with HDL cholesterol concentrations were not the same as those with the strongest association with kidney function and vice versa. An evaluation of pleiotropy indicated that the effects of the HDL-associated SNPs on eGFR were not mediated by HDL cholesterol. In addition, we performed a Mendelian randomization analysis. This analysis revealed a positive but nonsignificant causal effect of HDL cholesterol-increasing variants on eGFR. In summary, our findings indicate that HDL cholesterol does not causally influence eGFR and propose pleiotropic effects on eGFR for some HDL cholesterol-associated SNPs. This may cause the observed association by mechanisms other than the mere HDL cholesterol concentration. © 2016 The Authors.

Effective connectivity: Influence, causality and biophysical modeling

PubMed Central

Valdes-Sosa, Pedro A.; Roebroeck, Alard; Daunizeau, Jean; Friston, Karl

2011-01-01

This is the final paper in a Comments and Controversies series dedicated to “The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution”. We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener–Akaike–Granger–Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments. PMID:21477655

Causality attribution biases oculomotor responses.

PubMed

Badler, Jeremy; Lefèvre, Philippe; Missal, Marcus

2010-08-04

When viewing one object move after being struck by another, humans perceive that the action of the first object "caused" the motion of the second, not that the two events occurred independently. Although established as a perceptual and linguistic concept, it is not yet known whether the notion of causality exists as a fundamental, preattentional "Gestalt" that can influence predictive motor processes. Therefore, eye movements of human observers were measured while viewing a display in which a launcher impacted a tool to trigger the motion of a second "reaction" target. The reaction target could move either in the direction predicted by transfer of momentum after the collision ("causal") or in a different direction ("noncausal"), with equal probability. Control trials were also performed with identical target motion, either with a 100 ms time delay between the collision and reactive motion, or without the interposed tool. Subjects made significantly more predictive movements (smooth pursuit and saccades) in the causal direction during standard trials, and smooth pursuit latencies were also shorter overall. These trends were reduced or absent in control trials. In addition, pursuit latencies in the noncausal direction were longer during standard trials than during control trials. The results show that causal context has a strong influence on predictive movements.

Associations among Friendship Jealousy, Peer Status, and Relational Aggression in Early Adolescence

ERIC Educational Resources Information Center

Kraft, Caroline; Mayeux, Lara

2018-01-01

This study investigated the associations among peer status, friendship jealousy, and relational aggression in early adolescence, with a focus on peer status as a moderator of the association between relational aggression and friendship jealousy. Three hundred eighteen sixth-, seventh-, and eighth-grade students completed a sociometric assessment…

[Causality link in criminal law: role of epidemiology].

PubMed

Zocchetti, C; Riboldi, L

2003-01-01

This paper focusses on the role of epidemiology in demonstrating causality in criminal trials of toxic tort litigation. First of all, consideration is given of the specificity of the criminal trial and of the role of the epidemiologist as expert witness. As a second step the concept of causality is examined separating general from specific (individual level) causality. As regards general causality, strategies based on some criteria (example: Bradford-Hill criteria) are contrasted with approaches that do not consider causality a matter of science but one of health policy; and specific methods frequently used (meta-analysis, risk assessment, International Boards evaluation,....) are discussed, with special reference to the adoption of high-level standards and to the context of cross-examination. As regards individual level causality the difficulties of the epidemiologic approach to such evaluation are stressed, with special reference to topics like expected value, attributable risk, and probability of causation. All examples are taken from Italian court trials. A general comment on the difficulties of using the criminal trial (dominated by the "but for" rule) for toxic tort litigation and on the opportunity to switch to trials (civil, administrative) with less stringent causal rules ("more probable than not") is offered, with a consideration also of what are called "class actions".

Spot the difference: Causal contrasts in scientific diagrams.

PubMed

Scholl, Raphael

2016-12-01

An important function of scientific diagrams is to identify causal relationships. This commonly relies on contrasts that highlight the effects of specific difference-makers. However, causal contrast diagrams are not an obvious and easy to recognize category because they appear in many guises. In this paper, four case studies are presented to examine how causal contrast diagrams appear in a wide range of scientific reports, from experimental to observational and even purely theoretical studies. It is shown that causal contrasts can be expressed in starkly different formats, including photographs of complexly visualized macromolecules as well as line graphs, bar graphs, or plots of state spaces. Despite surface differences, however, there is a measure of conceptual unity among such diagrams. In empirical studies they often serve not only to infer and communicate specific causal claims, but also as evidence for them. The key data of some studies is given nowhere except in the diagrams. Many diagrams show multiple causal contrasts in order to demonstrate both that an effect exists and that the effect is specific - that is, to narrowly circumscribe the phenomenon to be explained. In a large range of scientific reports, causal contrast diagrams reflect the core epistemic claims of the researchers. Copyright © 2016. Published by Elsevier Ltd.

Pathway Analysis and the Search for Causal Mechanisms

ERIC Educational Resources Information Center

Weller, Nicholas; Barnes, Jeb

2016-01-01

The study of causal mechanisms interests scholars across the social sciences. Case studies can be a valuable tool in developing knowledge and hypotheses about how causal mechanisms function. The usefulness of case studies in the search for causal mechanisms depends on effective case selection, and there are few existing guidelines for selecting…

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

Causal inference in biology networks with integrated belief propagation.

PubMed

Chang, Rui; Karr, Jonathan R; Schadt, Eric E

2015-01-01

Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot.

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…

A general, multivariate definition of causal effects in epidemiology.

PubMed

Flanders, W Dana; Klein, Mitchel

2015-07-01

Population causal effects are often defined as contrasts of average individual-level counterfactual outcomes, comparing different exposure levels. Common examples include causal risk difference and risk ratios. These and most other examples emphasize effects on disease onset, a reflection of the usual epidemiological interest in disease occurrence. Exposure effects on other health characteristics, such as prevalence or conditional risk of a particular disability, can be important as well, but contrasts involving these other measures may often be dismissed as non-causal. For example, an observed prevalence ratio might often viewed as an estimator of a causal incidence ratio and hence subject to bias. In this manuscript, we provide and evaluate a definition of causal effects that generalizes those previously available. A key part of the generalization is that contrasts used in the definition can involve multivariate, counterfactual outcomes, rather than only univariate outcomes. An important consequence of our generalization is that, using it, one can properly define causal effects based on a wide variety of additional measures. Examples include causal prevalence ratios and differences and causal conditional risk ratios and differences. We illustrate how these additional measures can be useful, natural, easily estimated, and of public health importance. Furthermore, we discuss conditions for valid estimation of each type of causal effect, and how improper interpretation or inferences for the wrong target population can be sources of bias.

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.

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

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.

Causal Mediation Analysis: Warning! Assumptions Ahead

ERIC Educational Resources Information Center

Keele, Luke

2015-01-01

In policy evaluations, interest may focus on why a particular treatment works. One tool for understanding why treatments work is causal mediation analysis. In this essay, I focus on the assumptions needed to estimate mediation effects. I show that there is no "gold standard" method for the identification of causal mediation effects. In…

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…

On the road toward formal reasoning: reasoning with factual causal and contrary-to-fact causal premises during early adolescence.

PubMed

Markovits, Henry

2014-12-01

Understanding the development of conditional (if-then) reasoning is critical for theoretical and educational reasons. Here we examined the hypothesis that there is a developmental transition between reasoning with true and contrary-to-fact (CF) causal conditionals. A total of 535 students between 11 and 14 years of age received priming conditions designed to encourage use of either a true or CF alternatives generation strategy and reasoning problems with true causal and CF causal premises (with counterbalanced order). Results show that priming had no effect on reasoning with true causal premises. By contrast, priming with CF alternatives significantly improved logical reasoning with CF premises. Analysis of the effect of order showed that reasoning with CF premises reduced logical responding among younger students but had no effect among older students. Results support the idea that there is a transition in the reasoning processes in this age range associated with the nature of the alternatives generation process required for logical reasoning with true and CF causal conditionals. Copyright © 2014 Elsevier Inc. All rights reserved.

Quantum probability assignment limited by relativistic causality.

PubMed

Han, Yeong Deok; Choi, Taeseung

2016-03-14

Quantum theory has nonlocal correlations, which bothered Einstein, but found to satisfy relativistic causality. Correlation for a shared quantum state manifests itself, in the standard quantum framework, by joint probability distributions that can be obtained by applying state reduction and probability assignment that is called Born rule. Quantum correlations, which show nonlocality when the shared state has an entanglement, can be changed if we apply different probability assignment rule. As a result, the amount of nonlocality in quantum correlation will be changed. The issue is whether the change of the rule of quantum probability assignment breaks relativistic causality. We have shown that Born rule on quantum measurement is derived by requiring relativistic causality condition. This shows how the relativistic causality limits the upper bound of quantum nonlocality through quantum probability assignment.

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.

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.

Adolescent Weight Status: Associations With Structural and Functional Dimensions of Social Relations.

PubMed

Kjelgaard, Heidi Hjort; Holstein, Bjørn Evald; Due, Pernille; Brixval, Carina Sjöberg; Rasmussen, Mette

2017-04-01

To examine the associations between weight status and structural and functional dimensions of social relations among 11- to 15-year-old girls and boys. Analyses were based on cross-sectional data from the Danish contribution to the international Health Behavior in School-aged Children study 2010. The study population (n = 4,922) included students in the fifth, seventh, and ninth grade from a representative sample of Danish schools. Multinomial logistic regression analyses were used to study the associations between weight status and social relations, supported by a conceptual framework for the study of social relations. Among girls, overweight/obese weight status was associated with spending less time with friends after school compared to normal-weight status (0 days/week: odds ratio: 6.25, 95% confidence interval: 2.18-17.95, 1 day/week: 2.81, 1.02-7.77, 2 days/week: 3.27, 1.25-8.56, 3 days/week: 3.32, 1.28-8.61, and 4 days/week: 3.23, 1.17-8.92, respectively vs. 5 days/week). Among girls, overweight/obese weight status was associated with being bullied (2.62, 1.55-4.43). Among boys, overweight/obese weight status was associated with infrequent (1 to 2 days vs. every day) communication with friends through cellphones, SMS messages, or Internet (1.66, 1.03-2.67). In the full population, overweight/obese weight status was associated with not perceiving best friend as a confidant (1.59, 1.11-2.28). No associations were found between weight status and number of close same-sex and opposite-sex friends, mother/father as confidant, and perceived classmate acceptance. This study shows that overweight/obese adolescents have higher odds of numerous poor social relations than their normal-weight peers both in terms of structural and functional dimensions of social relations. Copyright © 2016 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Compact Representations of Extended Causal Models

ERIC Educational Resources Information Center

Halpern, Joseph Y.; Hitchcock, Christopher

2013-01-01

Judea Pearl (2000) was the first to propose a definition of actual causation using causal models. A number of authors have suggested that an adequate account of actual causation must appeal not only to causal structure but also to considerations of "normality." In Halpern and Hitchcock (2011), we offer a definition of actual causation…

The causal structure of utility conditionals.

PubMed

Bonnefon, Jean-François; Sloman, Steven A

2013-01-01

The psychology of reasoning is increasingly considering agents' values and preferences, achieving greater integration with judgment and decision making, social cognition, and moral reasoning. Some of this research investigates utility conditionals, ''if p then q'' statements where the realization of p or q or both is valued by some agents. Various approaches to utility conditionals share the assumption that reasoners make inferences from utility conditionals based on the comparison between the utility of p and the expected utility of q. This article introduces a new parameter in this analysis, the underlying causal structure of the conditional. Four experiments showed that causal structure moderated utility-informed conditional reasoning. These inferences were strongly invited when the underlying structure of the conditional was causal, and significantly less so when the underlying structure of the conditional was diagnostic. This asymmetry was only observed for conditionals in which the utility of q was clear, and disappeared when the utility of q was unclear. Thus, an adequate account of utility-informed inferences conditional reasoning requires three components: utility, probability, and causal structure. Copyright © 2012 Cognitive Science Society, Inc.

Sedentary behaviour and adiposity in youth: a systematic review of reviews and analysis of causality.

PubMed

Biddle, Stuart J H; García Bengoechea, Enrique; Wiesner, Glen

2017-03-28

Sedentary behaviour (sitting time) has becoming a very popular topic for research and translation since early studies on TV viewing in children in the 1980s. The most studied area for sedentary behaviour health outcomes has been adiposity in young people. However, the literature is replete with inconsistencies. We conducted a systematic review of systematic reviews and meta-analyses to provide a comprehensive analysis of evidence and state-of-the-art synthesis on whether sedentary behaviours are associated with adiposity in young people, and to what extent any association can be considered 'causal'. Searches yielded 29 systematic reviews of over 450 separate papers. We analysed results by observational (cross-sectional and longitudinal) and intervention designs. Small associations were reported for screen time and adiposity from cross-sectional evidence, but associations were less consistent from longitudinal studies. Studies using objective accelerometer measures of sedentary behaviour yielded null associations. Most studies assessed BMI/BMI-z. Interventions to reduce sedentary behaviour produced modest effects for weight status and adiposity. Accounting for effects from sedentary behaviour reduction alone is difficult as many interventions included additional changes in behaviour, such as physical activity and dietary intake. Analysis of causality guided by the classic Bradford Hill criteria concluded that there is no evidence for a causal association between sedentary behaviour and adiposity in youth, although a small dose-response association exists. Associations between sedentary behaviour and adiposity in children and adolescents are small to very small and there is little to no evidence that this association is causal. This remains a complex field with different exposure and outcome measures and research designs. But claims for 'clear' associations between sedentary behaviour and adiposity in youth, and certainly for causality, are premature or misguided.

Entanglement, holography and causal diamonds

NASA Astrophysics Data System (ADS)

de Boer, Jan; Haehl, Felix M.; Heller, Michal P.; Myers, Robert C.

2016-08-01

We argue that the degrees of freedom in a d-dimensional CFT can be reorganized in an insightful way by studying observables on the moduli space of causal diamonds (or equivalently, the space of pairs of timelike separated points). This 2 d-dimensional space naturally captures some of the fundamental nonlocality and causal structure inherent in the entanglement of CFT states. For any primary CFT operator, we construct an observable on this space, which is defined by smearing the associated one-point function over causal diamonds. Known examples of such quantities are the entanglement entropy of vacuum excitations and its higher spin generalizations. We show that in holographic CFTs, these observables are given by suitably defined integrals of dual bulk fields over the corresponding Ryu-Takayanagi minimal surfaces. Furthermore, we explain connections to the operator product expansion and the first law of entanglemententropy from this unifying point of view. We demonstrate that for small perturbations of the vacuum, our observables obey linear two-derivative equations of motion on the space of causal diamonds. In two dimensions, the latter is given by a product of two copies of a two-dimensional de Sitter space. For a class of universal states, we show that the entanglement entropy and its spin-three generalization obey nonlinear equations of motion with local interactions on this moduli space, which can be identified with Liouville and Toda equations, respectively. This suggests the possibility of extending the definition of our new observables beyond the linear level more generally and in such a way that they give rise to new dynamically interacting theories on the moduli space of causal diamonds. Various challenges one has to face in order to implement this idea are discussed.

Causal Analysis/Diagnosis Decision Information System (CADDIS)

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 to determine the cause of contamination.

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

Causal Responsibility and Counterfactuals

PubMed Central

Lagnado, David A; Gerstenberg, Tobias; Zultan, Ro'i

2013-01-01

How do people attribute responsibility in situations where the contributions of multiple agents combine to produce a joint outcome? The prevalence of over-determination in such cases makes this a difficult problem for counterfactual theories of causal responsibility. In this article, we explore a general framework for assigning responsibility in multiple agent contexts. We draw on the structural model account of actual causation (e.g., Halpern & Pearl, 2005) and its extension to responsibility judgments (Chockler & Halpern, 2004). We review the main theoretical and empirical issues that arise from this literature and propose a novel model of intuitive judgments of responsibility. This model is a function of both pivotality (whether an agent made a difference to the outcome) and criticality (how important the agent is perceived to be for the outcome, before any actions are taken). The model explains empirical results from previous studies and is supported by a new experiment that manipulates both pivotality and criticality. We also discuss possible extensions of this model to deal with a broader range of causal situations. Overall, our approach emphasizes the close interrelations between causality, counterfactuals, and responsibility attributions. PMID:23855451

On the origin of Hill's causal criteria.

PubMed

Morabia, A

1991-09-01

The rules to assess causation formulated by the eighteenth century Scottish philosopher David Hume are compared to Sir Austin Bradford Hill's causal criteria. The strength of the analogy between Hume's rules and Hill's causal criteria suggests that, irrespective of whether Hume's work was known to Hill or Hill's predecessors, Hume's thinking expresses a point of view still widely shared by contemporary epidemiologists. The lack of systematic experimental proof to causal inferences in epidemiology may explain the analogy of Hume's and Hill's, as opposed to Popper's, logic.

Dual Causality and the Autonomy of Biology.

PubMed

Bock, Walter J

2017-03-01

Ernst Mayr's concept of dual causality in biology with the two forms of causes (proximate and ultimate) continues to provide an essential foundation for the philosophy of biology. They are equivalent to functional (=proximate) and evolutionary (=ultimate) causes with both required for full biological explanations. The natural sciences can be classified into nomological, historical nomological and historical dual causality, the last including only biology. Because evolutionary causality is unique to biology and must be included for all complete biological explanations, biology is autonomous from the physical sciences.

Can chance cause cancer? A causal consideration.

PubMed

Stensrud, Mats Julius; Strohmaier, Susanne; Valberg, Morten; Aalen, Odd Olai

2017-04-01

The role of randomness, environment and genetics in cancer development is debated. We approach the discussion by using the potential outcomes framework for causal inference. By briefly considering the underlying assumptions, we suggest that the antagonising views arise due to estimation of substantially different causal effects. These effects may be hard to interpret, and the results cannot be immediately compared. Indeed, it is not clear whether it is possible to define a causal effect of chance at all. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

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.

Epidemiologic evidence for a causal relation between vaccination and fibrosarcoma tumorigenesis in cats.

PubMed

Kass, P H; Barnes, W G; Spangler, W L; Chomel, B B; Culbertson, M R

1993-08-01

Within the past 2 years, a putative causal relationship has been reported between vaccination against rabies and the development of fibrosarcomas at injection sites in cats. A retrospective study was undertaken, involving 345 cats with fibrosarcomas diagnosed between January 1991 and May 1992, to assess the causal hypothesis. Cats with fibrosarcomas developing at body locations where vaccines are typically administered (n = 185) were compared with controls (n = 160) having fibrosarcomas at locations not typically used for vaccination. In cats receiving FeLV vaccination within 2 years of tumorigenesis, the time between vaccination and tumor development was significantly (P = 0.005) shorter for tumors developing at sites where vaccines are typically administered than for tumors at other sites. Univariate analysis, adjusted for age, revealed associations between FeLV vaccination (odds ratio [OR] = 2.82; 95% confidence interval [CI] = 1.54 to 5.15), rabies vaccination at the cervical/interscapular region (OR = 2.09; 95% CI = 1.01 to 4.31), and rabies vaccination at the femoral region (OR = 1.83; 95% CI = 0.65 to 5.10) with fibrosarcoma development at the vaccination site within 1 year of vaccination. Multivariate analysis, adjusted for age and other vaccines, also revealed increased risks after FeLV (OR = 5.49; 95% CI = 1.98 to 15.24) and rabies (OR = 1.99; 95% CI = 0.72 to 5.54) vaccination.(ABSTRACT TRUNCATED AT 250 WORDS)

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.

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.

Vitamin D status and the risk of type 2 diabetes: the Melbourne Collaborative Cohort Study.

PubMed

Heath, Alicia K; Williamson, Elizabeth J; Hodge, Allison M; Ebeling, Peter R; Eyles, Darryl W; Kvaskoff, David; O'Dea, Kerin; Giles, Graham G; English, Dallas R

2018-05-18

Inverse associations between vitamin D status and risk of type 2 diabetes observed in epidemiological studies could be biased by confounding and reverse causality. We investigated the prospective association between vitamin D status and type 2 diabetes and the possible role of reverse causality. We conducted a case-cohort study within the Melbourne Collaborative Cohort Study (MCCS), including a random sample of 628 participants who developed diabetes and a sex-stratified random sample of the cohort (n=1,884). Concentration of 25-hydroxyvitamin D (25(OH)D) was measured using liquid chromatography-tandem mass spectrometry in samples collected at recruitment. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the risk of type 2 diabetes for quartiles of 25(OH)D relative to the lowest quartile and per 25 nmol/L increase in 25(OH)D, adjusting for confounding variables. The ORs for the highest versus lowest 25(OH)D quartile and per 25 nmol/L increase in 25(OH)D were 0.60 (95% CI: 0.44, 0.81) and 0.76 (95% CI: 0.63, 0.92; p=0.004), respectively. In participants who reported being in good/very good/excellent health approximately four years after recruitment, ORs for the highest versus lowest 25(OH)D quartile and per 25 nmol/L increase in 25(OH)D were 0.46 (95% CI: 0.29, 0.72) and 0.71 (95% CI: 0.56, 0.89; p=0.003), respectively. In this sample of middle-aged Australians, vitamin D status was inversely associated with the risk of type 2 diabetes, and this association did not appear to be explained by reverse causality. Copyright © 2018. Published by Elsevier B.V.

Identifying Causal Variants at Loci with Multiple Signals of Association

PubMed Central

Hormozdiari, Farhad; Kostem, Emrah; Kang, Eun Yong; Pasaniuc, Bogdan; Eskin, Eleazar

2014-01-01

Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility of an arbitrary number of causal variants when estimating the posterior probability of a variant being causal. A direct benefit of our approach is that we predict a set of variants for each locus that under reasonable assumptions will contain all of the true causal variants with a high confidence level (e.g., 95%) even when the locus contains multiple causal variants. We use simulations to show that our approach provides 20–50% improvement in our ability to identify the causal variants compared to the existing methods at loci harboring multiple causal variants. We validate our approach using empirical data from an expression QTL study of CHI3L2 to identify new causal variants that affect gene expression at this locus. CAVIAR is publicly available online at http://genetics.cs.ucla.edu/caviar/. PMID:25104515

Determining Directional Dependency in Causal Associations

ERIC Educational Resources Information Center

Pornprasertmanit, Sunthud; Little, Todd D.

2012-01-01

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…

Low Vitamin D Status: Definition, Prevalence, Consequences and Correction

PubMed Central

Binkley, Neil; Ramamurthy, Rekha; Krueger, Diane

2014-01-01

Vitamin D is obtained from cutaneous production when 7-dehydrocholesterol is converted to vitamin D3 (cholecalciferol) by ultraviolet B radiation or by oral intake of vitamin D2 (ergocalciferol) and D3. An individual's vitamin D status is best evaluated by measuring the circulating 25-hydroxyvitamin D [25(OH)D] concentration. Though controversy surrounds the definition of low vitamin D status, there is increasing agreement that the optimal circulating 25(OH)D level should be ~30-32 ng/ml or above. Using this definition, it has been is estimated that approximately three quarters of all adults in the United States are low. Classically, low vitamin D status has skeletal consequences such as osteomalacia/rickets. More recently, associations between low vitamin D status and increased risk for various non-skeletal morbidities have been recognized; whether all of these associations are causally related to low vitamin D status remains to be determined. To achieve optimal vitamin D status, daily intakes of at least 1000 IU or more of vitamin D are required. The risk of toxicity with “high” amounts of vitamin D intake is low. Substantial between-individual variability exists in response to the same administered vitamin D dose. When to monitor 25(OH)D levels has received little attention. Supplementation with vitamin D3 may be preferable to vitamin D2. PMID:20511052

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

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.

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

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.

Explaining quantum correlations through evolution of causal models

NASA Astrophysics Data System (ADS)

Harper, Robin; Chapman, Robert J.; Ferrie, Christopher; Granade, Christopher; Kueng, Richard; Naoumenko, Daniel; Flammia, Steven T.; Peruzzo, Alberto

2017-04-01

We propose a framework for the systematic and quantitative generalization of Bell's theorem using causal networks. We first consider the multiobjective optimization problem of matching observed data while minimizing the causal effect of nonlocal variables and prove an inequality for the optimal region that both strengthens and generalizes Bell's theorem. To solve the optimization problem (rather than simply bound it), we develop a genetic algorithm treating as individuals causal networks. By applying our algorithm to a photonic Bell experiment, we demonstrate the trade-off between the quantitative relaxation of one or more local causality assumptions and the ability of data to match quantum correlations.

[Affective bipolar disorder and ambivalence in relation to the drug treatment: analyzing the causal conditions].

PubMed

Miasso, Adriana Inocenti; Cassiani, Silvia Helena De Bortoli; Pedrão, Luiz Jorge

2011-04-01

This study was performed with an aim to understand the conditions causing the ambivalence of the person with bipolar affective disorder (BAD) regarding following the drug treatment. A qualitative approach was used, with the Grounded Theory as the methodology framework, under the light of Symbolic Interactionism. Participants were 14 individuals with BAD who were being followed at an Outpatient Clinic for Mood Disorders of a university hospital and 14 relatives they indicated. Interviews and observation were the main forms of obtaining data. Results revealed three categories that described the referred causal conditions: experiencing the crises of the disorder; needing the drug; and living with the side effects of the drugs. It was found that there is a need to change the attitude of some health professionals from blaming the patient for interrupting the treatment to one of listening, valuing their symbolic and affective universe as well as the partnership in the treatment.

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

Nutritional status, growth and sleep habits among Senegalese adolescent girls.

PubMed

Bénéfice, E; Garnier, D; Ndiaye, G

2004-02-01

To study the relation between sleep habits, nutritional status, growth and maturation in a group of African adolescent girls. The main hypothesis to be tested was that sleep length could be an effective way to spare energy, and thus malnourished girls sleep longer than normal girls. Three repeated yearly surveys (1997-1999) on a subsample of girls drawn from a larger study cohort on growth at adolescence. The Niakhar district in the central part of Senegal. In total, 40 girls were initially drawn. Missing girls were replaced at each round by girls having the same characteristics and belonging to the same cohort. At each round, data on pubertal development (breast stages and occurrence of menarche), growth and nutritional status were collected. Adolescents wore an accelerometer for three or four consecutive nights and days at each round. At the beginning of the survey, girls were 13.3+/-0.5 y old. They were under international reference values in weight and height. Their mean sleep duration was 8.5+/-0.9 h. Their puberty status did not influence their sleep habits; however, they slept more in March than in June, which was related to the seasonal change in daylight. There was a significant relation between body mass index and sleep habits: thinner girls slept a longer time and more quietly than the more corpulent girls. The nutritional status of these girls influenced their sleep habits: this may have been either a direct causal relation or a consequence of a protective attitude on the part of the mothers towards the frailer girls.

Health Insurance and Health Status: Exploring the Causal Effect from a Policy Intervention.

PubMed

Pan, Jay; Lei, Xiaoyan; Liu, Gordon G

2016-11-01

Whether health insurance matters for health has long been a central issue for debate when assessing the full value of health insurance coverage in both developed and developing countries. In 2007, the government-led Urban Resident Basic Medical Insurance (URBMI) program was piloted in China, followed by a nationwide implementation in 2009. Different premium subsidies by government across cities and groups provide a unique opportunity to employ the instrumental variables estimation approach to identify the causal effects of health insurance on health. Using a national panel survey of the URBMI, we find that URBMI beneficiaries experience statistically better health than the uninsured. Furthermore, the insurance health benefit appears to be stronger for groups with disadvantaged education and income than for their counterparts. In addition, the insured receive more and better inpatient care, without paying more for services. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Causal inference from observational data.

PubMed

Listl, Stefan; Jürges, Hendrik; Watt, Richard G

2016-10-01

Randomized controlled trials have long been considered the 'gold standard' for causal inference in clinical research. In the absence of randomized experiments, identification of reliable intervention points to improve oral health is often perceived as a challenge. But other fields of science, such as social science, have always been challenged by ethical constraints to conducting randomized controlled trials. Methods have been established to make causal inference using observational data, and these methods are becoming increasingly relevant in clinical medicine, health policy and public health research. This study provides an overview of state-of-the-art methods specifically designed for causal inference in observational data, including difference-in-differences (DiD) analyses, instrumental variables (IV), regression discontinuity designs (RDD) and fixed-effects panel data analysis. The described methods may be particularly useful in dental research, not least because of the increasing availability of routinely collected administrative data and electronic health records ('big data'). © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Identifying causal variants at loci with multiple signals of association.

PubMed

Hormozdiari, Farhad; Kostem, Emrah; Kang, Eun Yong; Pasaniuc, Bogdan; Eskin, Eleazar

2014-10-01

Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility of an arbitrary number of causal variants when estimating the posterior probability of a variant being causal. A direct benefit of our approach is that we predict a set of variants for each locus that under reasonable assumptions will contain all of the true causal variants with a high confidence level (e.g., 95%) even when the locus contains multiple causal variants. We use simulations to show that our approach provides 20-50% improvement in our ability to identify the causal variants compared to the existing methods at loci harboring multiple causal variants. We validate our approach using empirical data from an expression QTL study of CHI3L2 to identify new causal variants that affect gene expression at this locus. CAVIAR is publicly available online at http://genetics.cs.ucla.edu/caviar/. Copyright © 2014 by the Genetics Society of America.

Attributions of Social Causality and Responsibility.

DTIC Science & Technology

The paper reviews relevant research on attributions of causality and attributions of responsibility . It is suggested that inconsistencies among...findings in the attribution literature may be due to discrepancies between the meaning of ’ responsibility ’ and ’causality’. Definitions for the two terms...opposed to responsibility attribution may serve to eliminate some of the problems in attribution research. (Author)

Nonlinear parametric model for Granger causality of time series

NASA Astrophysics Data System (ADS)

Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

2006-06-01

The notion of Granger causality between two time series examines if the prediction of one series could be improved by incorporating information of the other. In particular, if the prediction error of the first time series is reduced by including measurements from the second time series, then the second time series is said to have a causal influence on the first one. We propose a radial basis function approach to nonlinear Granger causality. The proposed model is not constrained to be additive in variables from the two time series and can approximate any function of these variables, still being suitable to evaluate causality. Usefulness of this measure of causality is shown in two applications. In the first application, a physiological one, we consider time series of heart rate and blood pressure in congestive heart failure patients and patients affected by sepsis: we find that sepsis patients, unlike congestive heart failure patients, show symmetric causal relationships between the two time series. In the second application, we consider the feedback loop in a model of excitatory and inhibitory neurons: we find that in this system causality measures the combined influence of couplings and membrane time constants.

New Insights into Signed Path Coefficient Granger Causality Analysis

PubMed Central

Zhang, Jian; Li, Chong; Jiang, Tianzi

2016-01-01

Granger causality analysis, as a time series analysis technique derived from econometrics, has been applied in an ever-increasing number of publications in the field of neuroscience, including fMRI, EEG/MEG, and fNIRS. The present study mainly focuses on the validity of “signed path coefficient Granger causality,” a Granger-causality-derived analysis method that has been adopted by many fMRI researches in the last few years. This method generally estimates the causality effect among the time series by an order-1 autoregression, and defines a positive or negative coefficient as an “excitatory” or “inhibitory” influence. In the current work we conducted a series of computations from resting-state fMRI data and simulation experiments to illustrate the signed path coefficient method was flawed and untenable, due to the fact that the autoregressive coefficients were not always consistent with the real causal relationships and this would inevitablely lead to erroneous conclusions. Overall our findings suggested that the applicability of this kind of causality analysis was rather limited, hence researchers should be more cautious in applying the signed path coefficient Granger causality to fMRI data to avoid misinterpretation. PMID:27833547

New Insights into Signed Path Coefficient Granger Causality Analysis.

PubMed

Zhang, Jian; Li, Chong; Jiang, Tianzi

2016-01-01

Granger causality analysis, as a time series analysis technique derived from econometrics, has been applied in an ever-increasing number of publications in the field of neuroscience, including fMRI, EEG/MEG, and fNIRS. The present study mainly focuses on the validity of "signed path coefficient Granger causality," a Granger-causality-derived analysis method that has been adopted by many fMRI researches in the last few years. This method generally estimates the causality effect among the time series by an order-1 autoregression, and defines a positive or negative coefficient as an "excitatory" or "inhibitory" influence. In the current work we conducted a series of computations from resting-state fMRI data and simulation experiments to illustrate the signed path coefficient method was flawed and untenable, due to the fact that the autoregressive coefficients were not always consistent with the real causal relationships and this would inevitablely lead to erroneous conclusions. Overall our findings suggested that the applicability of this kind of causality analysis was rather limited, hence researchers should be more cautious in applying the signed path coefficient Granger causality to fMRI data to avoid misinterpretation.

Hemispheric lateralization in top-down attention during spatial relation processing: a Granger causal model approach.

PubMed

Falasca, N W; D'Ascenzo, S; Di Domenico, A; Onofrj, M; Tommasi, L; Laeng, B; Franciotti, R

2015-04-01

Magnetoencephalography was recorded during a matching-to-sample plus cueing paradigm, in which participants judged the occurrence of changes in either categorical (CAT) or coordinate (COO) spatial relations. Previously, parietal and frontal lobes were identified as key areas in processing spatial relations and it was shown that each hemisphere was differently involved and modulated by the scope of the attention window (e.g. a large and small cue). In this study, Granger analysis highlighted the patterns of causality among involved brain areas--the direction of information transfer ran from the frontal to the visual cortex in the right hemisphere, whereas it ran in the opposite direction in the left side. Thus, the right frontal area seems to exert top-down influence, supporting the idea that, in this task, top-down signals are selectively related to the right side. Additionally, for CAT change preceded by a small cue, the right frontal gyrus was not involved in the information transfer, indicating a selective specialization of the left hemisphere for this condition. The present findings strengthen the conclusion of the presence of a remarkable hemispheric specialization for spatial relation processing and illustrate the complex interactions between the lateralized parts of the neural network. Moreover, they illustrate how focusing attention over large or small regions of the visual field engages these lateralized networks differently, particularly in the frontal regions of each hemisphere, consistent with the theory that spatial relation judgements require a fronto-parietal network in the left hemisphere for categorical relations and on the right hemisphere for coordinate spatial processing. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Spatial separation of target and competitor cues enhances blocking of human causality judgements.

PubMed

Glautier, Steven

2002-04-01

Three experiments were carried out. Each required subjects to make judgements about the causal status of cues following a two-stage blocking procedure. In Stage 1 a competitor cue was consistently paired with an outcome, and in Stage 2 the competitor continued to be paired with the outcome but was accompanied by a target cue. It was predicted that causal judgements for the target would be reduced by the presence of the competitor. In Experiments 1 and 2 the blocking procedure was implemented as a computer simulation of a card game during which subjects had to learn which cards produced the best payouts. The cues that subjects used to make their judgement were colours and symbols that appeared on the backs of the cards. When the target and competitor cues appeared on the same card blocking effects did not emerge, but when they appeared as part of different cards blocking effects were found. Thus, spatial separation of target and competitor cues appeared to facilitate blocking. Experiment 3 replicated the blocking result using spatially separated target and competitor cues.

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 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.

Employment as a Social Determinant of Health: Exploring the Relationship between Neurocognitive Function and Employment Status

ERIC Educational Resources Information Center

Hergenrather, Kenneth C.; Emmanuel, Diona; McGuire-Kuletz, Maureen; Rhodes, Scott D.

2018-01-01

Purpose: To explore employment as a social determinant of health through examining the relationship between neurocognitive function and employment status. Method: The authors explored the causal relationship between employment status and neurocognitive function by conducting a systematic review of 15 longitudinal studies. The identified studies…

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.

Essays on Causal Inference for Public Policy

ERIC Educational Resources Information Center

Zajonc, Tristan

2012-01-01

Effective policymaking requires understanding the causal effects of competing proposals. Relevant causal quantities include proposals' expected effect on different groups of recipients, the impact of policies over time, the potential trade-offs between competing objectives, and, ultimately, the optimal policy. This dissertation studies causal…

Are Hill's criteria for causality satisfied for vitamin D and periodontal disease?

PubMed

Grant, William B; Boucher, Barbara J

2010-01-01

There is mounting evidence that periodontal disease (PD) is linked to low serum 25-hydroxyvitamin D [25(OH)D] concentrations in addition to recognized risk factors like diet and smoking. This paper reviews this evidence using Hill's criteria for causality in a biological system. Evidence for strength of association, consistency, cohesion and 'dose-effects' [biological 'gradients'] include strong inverse correlations between serum 25(OH) and PD cross-sectionally and that PD is consistently more prevalent in darker vs. lighter skinned people and increases at higher latitudes with analogy for gingivitis and for disorders associated with PD whose risks also increase with hypovitaminosis D. Evidence for plausibility includes that vitamin D increases calcium absorption and protects bone strength; induces formation of cathelicidin and other defensins that combat bacterial infection; reduces tissue production of destructive matrix metalloproteinases actively associated with PD and that prevalence of PD varies with common vitamin D receptor polymorphisms. Experimental evidence from limited supplementation studies [using calcium and vitamin D] shows that supplementation reduces tooth loss. Thus, existing evidence for hypovitaminosis D as a risk factor for PD to date meets Hill's criteria for causality in a biological system. Further experimental evidence for effectiveness and temporality, preferably from randomized controlled trials of vitamin D supplementation [adjusting for other PD risk factors including diet and smoking to reduce confounding] are necessary to confirm causality. If confirmed, dentists and periodontists could perform a valuable service to their patients by discussing the importance of adequate vitamin D status and how to avoid deficiency.

Are Hill's criteria for causality satisfied for vitamin D and periodontal disease?

PubMed Central

Boucher, Barbara J

2010-01-01

There is mounting evidence that periodontal disease (PD) is linked to low serum 25-hydroxyvitamin D [25(OH)D] concentrations in addition to recognized risk factors like diet and smoking. This paper reviews this evidence using Hill's criteria for causality in a biological system. Evidence for strength of association, consistency, cohesion and ‘dose-effects’ [biological ‘gradients’] include strong inverse correlations between serum 25(OH) and PD cross-sectionally and that PD is consistently more prevalent in darker vs. lighter skinned people and increases at higher latitudes with analogy for gingivitis and for disorders associated with PD whose risks also increase with hypovitaminosis D. Evidence for plausibility includes that vitamin D increases calcium absorption and protects bone strength; induces formation of cathelicidin and other defensins that combat bacterial infection; reduces tissue production of destructive matrix metalloproteinases actively associated with PD and that prevalence of PD varies with common vitamin D receptor polymorphisms. Experimental evidence from limited supplementation studies [using calcium and vitamin D] shows that supplementation reduces tooth loss. Thus, existing evidence for hypovitaminosis D as a risk factor for PD to date meets Hill's criteria for causality in a biological system. Further experimental evidence for effectiveness and temporality, preferably from randomized controlled trials of vitamin D supplementation [adjusting for other PD risk factors including diet and smoking to reduce confounding] are necessary to confirm causality. If confirmed, dentists and periodontists could perform a valuable service to their patients by discussing the importance of adequate vitamin D status and how to avoid deficiency. PMID:21547146

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.

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.

A Causal Model of Faculty Research Productivity.

ERIC Educational Resources Information Center

Bean, John P.

A causal model of faculty research productivity was developed through a survey of the literature. Models of organizational behavior, organizational effectiveness, and motivation were synthesized into a causal model of productivity. Two general types of variables were assumed to affect individual research productivity: institutional variables and…

Testing the causal theory of reference.

PubMed

Domaneschi, Filippo; Vignolo, Massimiliano; Di Paola, Simona

2017-04-01

Theories of reference are a crucial research topic in analytic philosophy. Since the publication of Kripke's Naming and Necessity, most philosophers have endorsed the causal/historical theory of reference. The goal of this paper is twofold: (i) to discuss a method for testing experimentally the causal theory of reference for proper names by investigating linguistic usage and (ii) to present the results from two experiments conducted with that method. Data collected in our experiments confirm the causal theory of reference for people proper names and for geographical proper names. A secondary but interesting result is that the semantic domain affects reference assignment: while with people proper names speakers tend to assign the semantic reference, with geographical proper names they are prompted to assign the speaker's reference. Copyright © 2016 Elsevier B.V. All rights reserved.

Relational trustworthiness: how status affects intra-organizational inequality in job autonomy.

PubMed

Campos-Castillo, Celeste; Ewoodzie, Kwesi

2014-03-01

Recent accounts of trustworthiness have moved away from treating it as a stable, individual-level attribute toward viewing it as a variable situated in a relational context, but have not been formalized or supported empirically. We extend status characteristics theory (SCT) to develop formal propositions about relational trustworthiness. We posit that members of task- and collectively oriented groups (non-consciously) infer three qualities from their relative status that are commonly used to determine an individual's trustworthiness: ability, benevolence, and integrity. We apply our formalization to clarify ambiguities regarding intra-organizational job autonomy inequality, thereby linking SCT to broader disparities rooted in job autonomy. We analyze data from a vignette experiment and the General Social Survey to test incrementally how well our propositions generalize across different settings and populations. Results generally support our proposed links between status and intra-organizational job autonomy. We discuss implications for SCT in understanding broader patterns of inequalities. Copyright © 2013 Elsevier Inc. All rights reserved.

An integrative systems genetics approach reveals potential causal genes and pathways related to obesity.

PubMed

Kogelman, Lisette J A; Zhernakova, Daria V; Westra, Harm-Jan; Cirera, Susanna; Fredholm, Merete; Franke, Lude; Kadarmideen, Haja N

2015-10-20

Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach. Using a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential expression analysis was performed using the Obesity Index as a continuous variable in a linear model. eQTL mapping was then performed to integrate 60 K porcine SNP chip data with the RNA sequencing data. Results were restricted based on genome-wide significant single nucleotide polymorphisms, detected differentially expressed genes, and previously detected co-expressed gene modules. Further data integration was performed by detecting co-expression patterns among eQTLs and integration with protein data. Differential expression analysis of RNA sequencing data revealed 458 differentially expressed genes. The eQTL mapping resulted in 987 cis-eQTLs and 73 trans-eQTLs (false discovery rate < 0.05), of which the cis-eQTLs were associated with metabolic pathways. We reduced the eQTL search space by focusing on differentially expressed and co-expressed genes and disease-associated single nucleotide polymorphisms to detect obesity-related genes and pathways. Building a co-expression network using eQTLs resulted in the detection of a module strongly associated with lipid pathways. Furthermore, we

Mind and Meaning: Piaget and Vygotsky on Causal Explanation.

ERIC Educational Resources Information Center

Beilin, Harry

1996-01-01

Piaget's theory has been characterized as descriptive and not explanatory, not qualifying as causal explanation. Piaget was consistent in showing how his theory was both explanatory and causal. Vygotsky also endorsed causal-genetic explanation but, on the basis of knowledge of only Piaget's earliest works, he claimed that Piaget's theory was not…

CADDIS Volume 5. Causal Databases: Home page (Duplicate?)

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.

Simplifying Causal Complexity: How Interactions between Modes of Causal Induction and Information Availability Lead to Heuristic-Driven Reasoning

ERIC Educational Resources Information Center

Grotzer, Tina A.; Tutwiler, M. Shane

2014-01-01

This article considers a set of well-researched default assumptions that people make in reasoning about complex causality and argues that, in part, they result from the forms of causal induction that we engage in and the type of information available in complex environments. It considers how information often falls outside our attentional frame…

Discovering Coherent Structures Using Local Causal States

NASA Astrophysics Data System (ADS)

Rupe, Adam; Crutchfield, James P.; Kashinath, Karthik; Prabhat, Mr.

2017-11-01

Coherent structures were introduced in the study of fluid dynamics and were initially defined as regions characterized by high levels of coherent vorticity, i.e. regions where instantaneously space and phase correlated vorticity are high. In a more general spatiotemporal setting, coherent structures can be seen as localized broken symmetries which persist in time. Building off the computational mechanics framework, which integrates tools from computation and information theory to capture pattern and structure in nonlinear dynamical systems, we introduce a theory of coherent structures, in the more general sense. Central to computational mechanics is the causal equivalence relation, and a local spatiotemporal generalization of it is used to construct the local causal states, which are utilized to uncover a system's spatiotemporal symmetries. Coherent structures are then identified as persistent, localized deviations from these symmetries. We illustrate how novel patterns and structures can be discovered in cellular automata and outline the path from them to laminar, transitional and turbulent flows. Funded by Intel through the Big Data Center at LBNL and the IPCC at UC Davis.

Causal effects of informal care and health on falls and other accidents among the elderly population in China.

PubMed

Wu, Hong; Lu, Naiji; Wang, Chenguang; Tu, Xinming

2018-03-01

This article analyzes the causal effects of informal care, mental health, and physical health on falls and other accidents (e.g., traffic accidents) among elderly people. We also examine if there are heterogeneous impacts on elderly of different gender, urban status, and past accident history. To purge potential reversal causal effects, e.g., past accidents induce more future informal care, we use two-stage least squares to identify the impacts. We use longitudinal data from a representative national China Health and Retirement Longitudinal Study of people aged 45 and older in China. A total of 3935 respondents with two-wave data are included in our study. Each respondent is interviewed to measure health status and report their accident history. Mental health is assessed using CES-D questions. Our findings indicate that while informal care decreased the occurrence of accidents, poor health conditions increase the occurrence of accidents. We also find heterogeneous impacts on the occurrence of accidents, varying by gender, urban status, and past accident history. Our findings suggest the following three policy implications. First, policy makers who aim to decrease accidents should take informal care of elders into account. Second, ease of birth policy and postponed retirement policy are urgently needed to meet the demands of informal care. Third, medical policies should attach great importance not only to physical health but also mental health of elderly parents especially for older people with accident history.

Updating during Reading Comprehension: Why Causality Matters

ERIC Educational Resources Information Center

Kendeou, Panayiota; Smith, Emily R.; O'Brien, Edward J.

2013-01-01

The present set of 7 experiments systematically examined the effectiveness of adding causal explanations to simple refutations in reducing or eliminating the impact of outdated information on subsequent comprehension. The addition of a single causal-explanation sentence to a refutation was sufficient to eliminate any measurable disruption in…

Causal Inferences in the Campbellian Validity System

ERIC Educational Resources Information Center

Lund, Thorleif

2010-01-01

The purpose of the present paper is to critically examine causal inferences and internal validity as defined by Campbell and co-workers. Several arguments are given against their counterfactual effect definition, and this effect definition should be considered inadequate for causal research in general. Moreover, their defined independence between…

Causal-Explanatory Pluralism: How Intentions, Functions, and Mechanisms Influence Causal Ascriptions

ERIC Educational Resources Information Center

Lombrozo, Tania

2010-01-01

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…

The Relations of Migrant Status and Parenting to Chinese Adolescents' Adjustment

ERIC Educational Resources Information Center

Zhang, Guangzhen; Eisenberg, Nancy; Liang, Zongbao; Li, Yi; Deng, Huihua

2017-01-01

The main goals of the present study were (a) to compare Chinese migrant and nonmigrant adolescents on mean levels of parenting, positive adjustment, and academic functioning, and to assess whether socioeconomic status (SES) accounted for any obtained differences, (b) to examine whether the relations of SES and migrant status to youths' positive…

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.

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

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.

Causal inference in survival analysis using pseudo-observations.

PubMed