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Sample records for aucune relation causale

  1. Preschoolers' Understanding of Temporal and Causal Relations.

    ERIC Educational Resources Information Center

    Sharp, Kay Colby

    1982-01-01

    Two experiments investigated psychological factors determining preschoolers' success or failure on a sequence-completion task involving temporal and causal ordering of events. Overall findings demonstrate that while preschoolers do understand temporal/causal relationships, their abilities are dependent on process variables demanded by the task…

  2. Causal Coherence Relations and Levels of Discourse Representation

    ERIC Educational Resources Information Center

    Mulder, Gerben; Sanders, Ted J. M.

    2012-01-01

    This study focused on the cognitive representation of causal coherence relations linguistically marked with the connective "because." This article investigated whether these local causal relations are represented both at the level of the textbase and the situation model. Following earlier studies investigating the psychological validity of levels…

  3. Causal Relations Drive Young Children's Induction, Naming, and Categorization

    ERIC Educational Resources Information Center

    Opfer, John E.; Bulloch, Megan J.

    2007-01-01

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

  4. 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.

  5. The Recovery of Locality for Causal Sets and Related Topics

    NASA Astrophysics Data System (ADS)

    Daughton, Alan Ronald

    The theory of causal sets is an attempt at a successful quantum theory of gravity. It is widely expected that any theory of quantum gravity should give rise to a discrete structure to spacetime geometry. This supposition rests upon three features of successfully quantized theories. The first is that quantization usually leads to some form of discrete object, or quantum, which is the source of the term "quantization". The second is the need for renormalization of field theories (which is not alleviated by including gravity, using the usual perturbative approach to quantization, as had once been hoped). An ultraviolet cutoff is introduced, which can be imagined to be either the mathematical manifestation of an underlying theory that contains new structures at some scale (for example the "GUT" fields, strings, etc.) or simply the result of the breakdown of the manifold picture at this same scale. Thirdly, quantized fields exhibit the phenomenon of virtual particles of unlimited energies at short length scales. The coupling of gravity to such quantized fields should induce infinite curvatures of the manifold at short scales, thereby dynamically prohibiting a manifold structure. The theory of causal sets begins with a discrete structure. The classical spacetime manifold is expected to emerge as an approximation (in a large grain limit) to the class of causal sets which dominate a sum over causal sets, each causal set being weighted by the exponentiation of an appropriately defined "action functional". In order for this theory to be successful, several questions need to be addressed. Some of these are directed at the relationship between the causal set theory and the geometry of classical spacetime. Other questions are related to the quantization of the causal set theory and inclusion of quantum fields into the theory's framework. This work addresses the relation between causal sets and classical gravity (specifically the issues of embeddability of a certain class of

  6. Causality and Kramers-Kronig relations for waveguides.

    PubMed

    Haakestad, Magnus; Skaar, Johannes

    2005-11-28

    Starting from the condition that optical signals propagate causally, we derive Kramers-Kronig relations for waveguides. For hollow waveguides with perfectly conductive walls, the modes propagate causally and Kramers-Kronig relations between the real and imaginary part of the mode indices exist. For dielectric waveguides, there exists a Kramers-Kronig type relation between the real mode index of a guided mode and the imaginary mode indices associated with the evanescent modes. For weakly guiding waveguides, the Kramers-Kronig relations are particularly simple, as the modal dispersion is determined solely from the profile of the corresponding mode field.

  7. Physical constraints on causality-violating spacetimes in general relativity

    NASA Astrophysics Data System (ADS)

    Janca, Andrew Joseph

    The theoretical possibility of global causality violation has long been a problem within general relativity, for there exists a large number of model spacetimes known to admit closed time-like curves, trajectories allowing a timelike observer to return to some point in her own past. However, nearly all such known models have some unphysical feature. These physicality issues rendered causality-violation to the status of an interesting but safely theoretical problem until twenty years ago, when the appearance of a new type of causality-violating model spacetime and the subsequent proliferation of new models admitting closed timelike curves forced the attention of the community to the issue, and made causality violation and its possible physical consequences an active area of research within general relativity. This paper focuses on some of the older causality-violating spacetimes which model matter sources with cylindrical symmetry. By describing how cylindrically-symmetric solutions can be embedded within a spatially bounded and physically realistic body which outwardly has the symmetry of a torus or ring, it is shown that the chief problem of physical plausibility which these older solutions possess can be resolved. The intention is to make these models active candidates for consideration in future experiments to test general relativity's prediction that causality violation is a phenomenon that could be observed in the real world. Attending chapters describe physical systems other than rotating objects that can alter a local observer's experience of time to a substantial extent, including an electrically-charged massive shell slowing time in its interior (though not affecting causality) and a class of trajectories in the Reissner-Nordstrom background that could in principle allow a timelike observer to reverse her personal arrow of time relative to other observers in the spacetime as a whole. The paper concludes with a discussion of one of the plausibility problems

  8. The causal relation between turbulent particle flux and density gradient

    NASA Astrophysics Data System (ADS)

    van Milligen, B. Ph.; Carreras, B. A.; García, L.; Martín de Aguilera, A.; Hidalgo, C.; Nicolau, J. H.

    2016-07-01

    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 and instantaneous, as is sometimes assumed.

  9. Relative entropy, mixed gauge-gravitational anomaly and causality

    NASA Astrophysics Data System (ADS)

    Bhattacharyya, Arpan; Cheng, Long; Hung, Ling-Yan

    2016-07-01

    In this note we explored the holographic relative entropy in the presence of the 5d Chern-Simons term, which introduces a mixed gauge-gravity anomaly to the dual CFT. The theory trivially satisfies an entanglement first law. However, to quadratic order in perturbations of the stress tensor T and current density J , there is a mixed contributionto the relative entropy bi-linear in T and J , signalling a potential violation of the positivity of the relative entropy. Miraculously, the term vanishes up to linear order in a derivative expansion. This prompted a closer inspection on a different consistency check, that involves time-delay of a graviton propagating in a charged background, scattered via a coupling supplied by the Chern-Simons term. The analysis suggests that the time-delay can take either sign, potentially violating causality for any finite value of the CS coupling.

  10. When Work is Related to Disease, What Establishes Evidence for a Causal Relation?

    PubMed Central

    2012-01-01

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

  11. Conceptions of Statistical Relations within the Context of Thinking about Causal Relations. 1984:03.

    ERIC Educational Resources Information Center

    Svensson, Lennart

    The results concerning conceptions of statistical relations, presented in this report are based on interviews with fourteen nurses and fourteen technicians. The interviews were about a medical and a technical case of a causal relation. The starting point was a statement about the existence of the relation and the subjects were asked what evidence…

  12. The causal relation between human papillomavirus and cervical cancer

    PubMed Central

    Bosch, F X; Lorincz, A; Muñoz, N; Meijer, C J L M; Shah, K V

    2002-01-01

    The causal role of human papillomavirus infections in cervical cancer has been documented beyond reasonable doubt. The association is present in virtually all cervical cancer cases worldwide. It is the right time for medical societies and public health regulators to consider this evidence and to define its preventive and clinical implications. A comprehensive review of key studies and results is presented. PMID:11919208

  13. Human sensitivity to the magnitude and probability of a continuous causal relation in a video game.

    PubMed

    Young, Michael E; Cole, James J

    2012-01-01

    Continuous causation, in which incremental changes in one variable cause incremental changes in another, has received little attention in the causal judgment literature. A video game was adapted for the study of continuous causality in order to examine the novel cues to causality that are present in these paradigms. The spatial proximity of an object to an "enemy detector" produced auditory responses as a function of the object's proximity. Participants' behavior was a function of the range of the effect's auditory sensitivity and the moment-to-moment likelihood of detection. This new paradigm provides a rich platform for examining the cues to causation encountered in the learning of continuous causal relations.

  14. Is there a causal relation between mathematical creativity and mathematical problem-solving performance?

    NASA Astrophysics Data System (ADS)

    Tyagi, Tarun Kumar

    2016-04-01

    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 eighth-grade students were analysed using a cross-lagged panel correlation (CLPC) design. CLPC attempts to rule out plausible alternative explanation of a causal effect. The result suggests that significant predominant causal relationship was found between MC and MP. It indicates that MP was found to be a cause of MC than the converse.

  15. [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.

  16. [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. PMID:24871883

  17. 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. PMID:27113431

  18. 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.

  19. 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

  20. Is preschool executive function causally related to academic achievement?

    PubMed

    Willoughby, Michael T; Kupersmidt, Janis B; Voegler-Lee, Mary E

    2012-01-01

    The primary objective of this study was to reevaluate the well-established result that preschoolers' performance on executive function tasks are positively associated with their performance on academic achievement tests. The current study replicated the previously established concurrent associations between children's performance on EF tasks and academic achievement tests. Specifically, children's performance on measures of inhibitory and motor control were positively associated with their performance on tests of reading, writing, and mathematics achievement (rs = .2-.5); moreover, although diminished in magnitude, most of these associations held up even after including an earlier measure of academic achievement as a covariate (rs = .1-.3). However, the application of an alternative analytic method, fixed effects analysis, a method that capitalizes on repeated measures data to control for all time stable measured and unmeasured covariates, rendered the apparent positive associations between executive function and academic achievement nonsignificant (rs = .0-.1). Taken together, these results suggest that the well-replicated association between executive function abilities and academic achievement may be spurious. Results are discussed with respect to the importance of utilizing analytic methods and research designs that facilitate strong causal inferences between executive function and academic achievement in early childhood, as well as the limitations of making curriculum development recommendations and/or public policy decisions based on studies that have failed to do so. PMID:21707258

  1. 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…

  2. 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…

  3. 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

  4. 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.

  5. Causal Inference Based on the Analysis of Events of Relations for Non-stationary Variables.

    PubMed

    Yin, Yu; Yao, Dezhong

    2016-01-01

    The main concept behind causality involves both statistical conditions and temporal relations. However, current approaches to causal inference, focusing on the probability vs. conditional probability contrast, are based on model functions or parametric estimation. These approaches are not appropriate when addressing non-stationary variables. In this work, we propose a causal inference approach based on the analysis of Events of Relations (CER). CER focuses on the temporal delay relation between cause and effect, and a binomial test is established to determine whether an "event of relation" with a non-zero delay is significantly different from one with zero delay. Because CER avoids parameter estimation of non-stationary variables per se, the method can be applied to both stationary and non-stationary signals. PMID:27389921

  6. Causal Inference Based on the Analysis of Events of Relations for Non-stationary Variables

    PubMed Central

    Yin, Yu; Yao, Dezhong

    2016-01-01

    The main concept behind causality involves both statistical conditions and temporal relations. However, current approaches to causal inference, focusing on the probability vs. conditional probability contrast, are based on model functions or parametric estimation. These approaches are not appropriate when addressing non-stationary variables. In this work, we propose a causal inference approach based on the analysis of Events of Relations (CER). CER focuses on the temporal delay relation between cause and effect, and a binomial test is established to determine whether an “event of relation” with a non-zero delay is significantly different from one with zero delay. Because CER avoids parameter estimation of non-stationary variables per se, the method can be applied to both stationary and non-stationary signals. PMID:27389921

  7. How Hypertext Reading Sequences Affect Understanding of Causal and Temporal Relations in Story Comprehension

    ERIC Educational Resources Information Center

    Urakami, Jacqueline; Krems, Josef F.

    2012-01-01

    The goal of this study is to examine the comprehension of global causal and temporal relations between events that are represented in single hypertext documents. In two experiments we examined how reading sequences of hypertext nodes affects the establishment of event relations and how this process can be supported by advanced organizers that…

  8. Perceived causal relations between anxiety, posttraumatic stress and depression: extension to moderation, mediation, and network analysis

    PubMed Central

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

    2013-01-01

    Background Previous research demonstrates that posttraumatic memory reexperiencing, depression, anxiety, and guilt-shame are frequently co-occurring problems that may be causally related. Objectives 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. Methods 288 young adults rated the frequency and respective PCR scores associating their symptoms of posttraumatic reexperiencing, depression, anxiety, and guilt-shame. Results 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. Conclusion 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. PMID:24003362

  9. Semi-supervised learning of causal relations in biomedical scientific discourse

    PubMed Central

    2014-01-01

    Background The increasing number of daily published articles in the biomedical domain has become too large for humans to handle on their own. As a result, bio-text mining technologies have been developed to improve their workload by automatically analysing the text and extracting important knowledge. Specific bio-entities, bio-events between these and facts can now be recognised with sufficient accuracy and are widely used by biomedical researchers. However, understanding how the extracted facts are connected in text is an extremely difficult task, which cannot be easily tackled by machinery. Results In this article, we describe our method to recognise causal triggers and their arguments in biomedical scientific discourse. We introduce new features and show that a self-learning approach improves the performance obtained by supervised machine learners to 83.47% for causal triggers. Furthermore, the spans of causal arguments can be recognised to a slightly higher level that by using supervised or rule-based methods that have been employed before. Conclusion Exploiting the large amount of unlabelled data that is already available can help improve the performance of recognising causal discourse relations in the biomedical domain. This improvement will further benefit the development of multiple tasks, such as hypothesis generation for experimental laboratories, contradiction detection, and the creation of causal networks. PMID:25559746

  10. Causal estimation of neural and overall baroreflex sensitivity in relation to carotid artery stiffness.

    PubMed

    Lipponen, Jukka A; Tarvainen, Mika P; Laitinen, Tomi; Karjalainen, Pasi A; Vanninen, Joonas; Koponen, Timo; Laitinen, Tiina M

    2013-12-01

    Continuous electrocardiogram, blood pressure and carotid artery ultrasound video were analyzed from 15 diabetics and 28 healthy controls. By using these measurements artery elasticity, overall baroreflex sensitivity (BRS) assessed between RR and systolic blood pressure variation, and neural BRS assessed between RR and artery diameter variation were estimated. In addition, BRS was estimated using traditional and causal methods which enable separation of feedforward and feedback variation. The aim of this study was to analyze overall and neural BRS in relation to artery stiffness and to validate the causal BRS estimation method in assessing these two types of BRS within the study population. The most significant difference between the healthy and diabetic groups (p < 0.0007) was found for the overall BRS estimated using the causal method. The difference between the groups was also significant for neural BRS (p < 0.0018). However neural BRS was normal in some old diabetics, which indicates normal functioning of autonomic nervous system (ANS), even though the elasticity in arteries of these subjects was reduced. The noncausal method overestimated neural BRS in low BRS values when compared to causal BRS. In conclusion, neural BRS estimated using the causal method is proposed as the best marker of ANS functioning. PMID:24168896

  11. Causal and causally separable processes

    NASA Astrophysics Data System (ADS)

    Oreshkov, Ognyan; Giarmatzi, Christina

    2016-09-01

    The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A and B, either A is in the causal past of B, B is in the causal past of A, or A and B are space-like separated. Operationally, the meaning of these order relations corresponds to constraints on the possible correlations between experiments performed in the vicinities of the respective events: if A is in the causal past of B, an experimenter at A could signal to an experimenter at B but not the other way around, while if A and B are space-like separated, no signaling is possible in either direction. In the context of a concrete physical theory, the correlations compatible with a given causal configuration may obey further constraints. For instance, space-like correlations in quantum mechanics arise from local measurements on joint quantum states, while time-like correlations are established via quantum channels. Similarly to other variables, however, the causal order of a set of events could be random, and little is understood about the constraints that causality implies in this case. A main difficulty concerns the fact that the order of events can now generally depend on the operations performed at the locations of these events, since, for instance, an operation at A could influence the order in which B and C occur in A’s future. So far, no formal theory of causality compatible with such dynamical causal order has been developed. Apart from being of fundamental interest in the context of inferring causal relations, such a theory is imperative for understanding recent suggestions that the causal order of events in quantum mechanics can be indefinite. Here, we develop such a theory in the general multipartite case. Starting from a background-independent definition of causality, we derive an iteratively formulated canonical decomposition of multipartite causal correlations. For a fixed number of settings and

  12. An Analysis of the Ontological Causal Relation in Physics and Its Educational Implications

    NASA Astrophysics Data System (ADS)

    Cheong, Yong Wook

    2016-08-01

    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 three issues: the appropriate view concerning ontological category, the role and status of ontological causal relations, and university students' understanding of the role and status of these relations. Concerning the issue of proper ontology, this study suggests an alternative view that distinguishes between interaction and property at the macroscopic level, in contrast to Chi and colleagues' influential view. Concerning the role and status of the relations, we conclude that fundamental ontological causal relations should be regarded as knowledge at the core of relevant physics theories. However, upon analysis of participants' responses, this study finds that university students' views on the status of the heat capacity relation and Newton's second law are quite different. Several possible educational implications of these results are discussed.

  13. Decision-related activity in sensory neurons reflects more than a neuron's causal effect.

    PubMed

    Nienborg, Hendrikje; Cumming, Bruce G

    2009-05-01

    During perceptual decisions, the activity of sensory neurons correlates with a subject's percept, even when the physical stimulus is identical. The origin of this correlation is unknown. Current theory proposes a causal effect of noise in sensory neurons on perceptual decisions, but the correlation could result from different brain states associated with the perceptual choice (a top-down explanation). These two schemes have very different implications for the role of sensory neurons in forming decisions. Here we use white-noise analysis to measure tuning functions of V2 neurons associated with choice and simultaneously measure how the variation in the stimulus affects the subjects' (two macaques) perceptual decisions. In causal models, stronger effects of the stimulus upon decisions, mediated by sensory neurons, are associated with stronger choice-related activity. However, we find that over the time course of the trial these measures change in different directions-at odds with causal models. An analysis of the effect of reward size also supports this conclusion. Finally, we find that choice is associated with changes in neuronal gain that are incompatible with causal models. All three results are readily explained if choice is associated with changes in neuronal gain caused by top-down phenomena that closely resemble attention. We conclude that top-down processes contribute to choice-related activity. Thus, even forming simple sensory decisions involves complex interactions between cognitive processes and sensory neurons.

  14. On the neglect of causality principles in solar activity - climate relations.

    NASA Astrophysics Data System (ADS)

    Stauning, Peter

    2010-05-01

    Many research papers have claimed to demonstrate close relations between solar activity and the terrestrial climate. In most cases the relations have been based on comparisons between time series of solar activity parameters, for instance sunspot numbers, and climate parameters, for instance terrestrial temperatures. However, many of the reported close relations are based on skilfully manipulated data and neglect of basic causality principles. For cause-effect relations to be reliably established, the cause must obviously happen prior to the effects. Thus it is problematic to use, for instance, running averages of parameters if the result depends too much on posterior elements of the causative time series or precursory elements of the effects. Even more neglected are the causality principles for cause-effect relations with a strongly varying source function. Damping of source variations by smoothing data handling introduces additional implied delays, which should be considered in the judgement of apparent correlations between processed time series of cause and effect parameters. The presentation will discuss examples of frequently quoted solar activity-climate relations (e.g., by Reid, Friis-Christensen, and Svensmark), which violate basic causality principles.

  15. The Processing of Causal and Hierarchical Relations in Semantic Memory as Revealed by N400 and Frontal Negativity.

    PubMed

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

    2015-01-01

    Most current studies investigating semantic memory have focused on associative (ring-emerald) or taxonomic relations (bird-sparrow). Little is known about the question of how causal relations (virus-epidemic) are stored and accessed in semantic memory. The goal of this study was to examine the processing of causally related, general associatively related and hierarchically related word pairs when participants were required to evaluate whether pairs of words were related in any way. The ERP data showed that the N400 amplitude (200-500 ms) elicited by unrelated related words was more negative than all related words. Furthermore, the late frontal distributed negativity (500-700 ms) elicited by causally related words was smaller than hierarchically related words, but not for general associated words. These results suggested the processing of causal relations and hierarchical relations in semantic memory recruited different degrees of cognitive resources, especially for role binding. PMID:26148338

  16. Bayesian modeling for linking causally related observations in chest X-ray reports.

    PubMed Central

    Chapman, W. W.; Haug, P. J.

    1998-01-01

    Our natural language understanding system outputs a list of diseases, findings, and appliances found in a chest x-ray report. The system described in this paper links those diseases and findings that are causally related. Using Bayesian networks to model the conceptual and diagnostic information found in a chest x-ray we are able to infer more specific information about the findings that are linked to diseases. PMID:9929287

  17. 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

  18. 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

  19. On the violation of causal, emotional, and locative inferences: An event-related potentials study.

    PubMed

    Rodríguez-Gómez, Pablo; Sánchez-Carmona, Alberto; Smith, Cybelle; Pozo, Miguel A; Hinojosa, José A; Moreno, Eva M

    2016-07-01

    Previous event-related potential studies have demonstrated the online generation of inferences during reading for comprehension tasks. The present study contrasted the brainwave patterns of activity to the fulfilment or violation of various types of inferences (causal, emotional, locative). Relative to inference congruent sentence endings, a typical centro-parietal N400 was elicited for the violation of causal and locative inferences. This N400 effect was initially absent for emotional inferences, most likely due to their lower cloze probability. Between 500 and 750ms, a larger frontal positivity (pN400FP) was elicited by inference incongruent sentence endings in the causal condition. In emotional sentences, both inference congruent and incongruent endings exerted this frontally distributed late positivity. For the violation of locative inferences, the larger positivity was only marginally significant over left posterior scalp locations. Thus, not all inference eliciting sentences evoked a similar pattern of ERP responses. We interpret and discuss our results in line with recent views on what the N400, the P600 and the pN400FP brainwave potentials index.

  20. 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.

  1. 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

  2. Tropical convective onset statistics and establishing causality in the water vapor-precipitation relation

    NASA Astrophysics Data System (ADS)

    Neelin, J. D.; Kuo, Y. H.; Schiro, K. A.; Langenbrunner, B.; Mechoso, C. R.; Sahany, S.; Bernstein, D. N.

    2015-12-01

    Previous work by various authors has pointed to the role of humidity in the lower free troposphere in affecting the onset of deep convection in the tropics. Empirical relations between column water vapor and the onset of precipitation have been inferred to be related to this. Evidence includes deep-convective conditional instability calculations for entraining plumes, in which the lower free-tropospheric environment affects the onset of deep convection due to the impact on buoyancy of turbulent entrainment of dry versus moist air. Tropical Western Pacific in situ observations, and tropical ocean basin satellite retrievals in comparison to climate model diagnostics each indicate that substantial entrainment is required to explain the observed relationship. In situ observations from the GoAmazon field campaign confirm that the basic relationship holds over tropical land much as it does over tropical ocean (although with greater additional sensitivity to boundary layer variations and to freezing processes). The relationship between deep convection and water vapor is, however, a two-way street, with convection moistening the free troposphere. One might thus argue that there has not yet been a smoking gun in terms of establishing the causality of the precipitation-water vapor relationship. Parameter perturbation experiments in the coupled Community Earth System Model show that when the deep convective scheme has low values of entrainment, the set of statistics associated with the transition to deep convection are radically altered, and the observed pickup of precipitation with column water vapor is no longer seen. In addition to cementing the dominant direction of causality in the fast timescale precipitation-column water vapor relationship, the results point to impacts of this mechanism on the climatology. Because at low entrainment the convection can fire before the lower troposphere is moistened, the climatology of water vapor remains lower than observed. These

  3. Intervention research, theoretical mechanisms, and causal processes related to externalizing behavior patterns.

    PubMed

    Hinshaw, Stephen P

    2002-01-01

    Intervention research with children and adolescents has suffered from a dearth of relevant theoretical grounding and from the lack of a reciprocal "feedback" mechanism by which clinical trials can inform relevant theorizing and conceptualization. There are hopeful signs, however, of increasing confluence between clinical efforts and theoretical models. Indeed, the key issue I discuss is how intervention studies can yield information about developmental and clinical theory as well as mechanisms related to psychopathology. Specific research examples in the field, particularly those emanating from the Multimodal Treatment Study of Children with attention deficit/hyperactivity disorder (MTA study), reveal that probes of moderator and mediator variables can clearly enhance our knowledge of relevant processes and mechanisms. In fact, recent MTA findings have relevance for models of genetic and epigenetic influence on symptomatology related to attentional deficits and hyperactivity. It would be overzealous, however, to make premature claims regarding etiologic variables from intervention research, as treatment studies typically address variables that are causally far "downstream" from primary causal factors and most clinical trials have statistical power that is barely sufficient for main outcome questions, much less mediational linkages. Overall, the field has severely underutilized experimental intervention research to subserve the dual ends of improving the lives of youth and advancing theoretical conceptualization regarding development and psychopathology.

  4. 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. PMID:27343481

  5. Nonultralocality and causality in the relational framework of canonical quantum gravity

    NASA Astrophysics Data System (ADS)

    de Vegvar, P. G. N.

    2016-05-01

    The relational framework of canonical quantum gravity with nonultralocal constraints is explored. After demonstrating the absence of anomalies, a spatially discretized version of the relational framework is introduced. This allows the application of Lieb-Robinson bounds to on-shell monotonic gauge flow when there is a continuous external "time" parameter. An explicit Lieb-Robinson bound is derived for the differential on-shell evolution of the operator norm of the commutator of discretized Dirac observables, demonstrating how a local light conelike causal structure emerges. Ultralocal constraints do not permit such a structure to arise via Lieb-Robinson bounds. Gauge and (3 +1 )-diffeomorphism invariance of the light cone is discussed along with the issues of quantum fluctuations, the nature of the nonlocalities, the spatial continuum limit, and the possible links to noncommutative geometry.

  6. Negative emotions and coronary heart disease: causally related or merely coexistent? A review.

    PubMed

    Smith, D F

    2001-02-01

    Negative emotions have been claimed to be a cause of coronary heart disease (CHD) as well as a consequence of cardiovascular disorders. Early case studies of cardiac disorders of soldiers in battle drew attention to the possibility that strong negative emotional states could cause CHD. Subsequent reports of reactions to natural disasters supported the notion that intense negative emotions could precipitate somatic disorders such as CHD. Since then, numerous studies have investigated relations between negative emotions and CHD. Over the years, retrospective studies have found, for example, that negative emotions are often present before the occurrence of CHD. Cross-sectional studies have indicated that symptoms of depression and anxiety are often present in CHD patients. Prospective studies have shown that the likelihood of CHD tends to be higher for people with negative emotions than for those without them. The main symptoms of negative emotional states that seem to be most closely associated with CHD are nervousness, getting easily upset, feeling fatigue, being indecisive, having sleep disturbances, being usually worried about something, and feeling that others would be better off if oneself were dead. Although the findings appear to support the notion of causal connections between negative emotions and CHD, they fail to provide conclusive proof of such relations. An alternative explanation that could also account for the findings is simply that negative emotions and CHD often coexist.

  7. 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. PMID:24840204

  8. Causal Judgments of Positive Mood in Relation to Self-Regulation: A Case Study with Flemish Students

    ERIC Educational Resources Information Center

    Human-Vogel, Salome; van Petegem, Peter

    2008-01-01

    To examine students' causal judgements of positive mood in relation to self-regulation, 128 participants from two different schools representing two distinct educational environments (Technical/Vocational School (TSO/BSO): N = 63; General Secondary School (ASO): N = 65) were asked to judge 45 statements containing three possible relationships (A…

  9. Causal Factors and Adverse Events of Aviation Accidents and Incidents Related to Integrated Vehicle Health Management

    NASA Technical Reports Server (NTRS)

    Reveley, Mary S.; Briggs, Jeffrey L.; Evans, Joni K.; Jones, Sharon M.; Kurtoglu, Tolga; Leone, Karen M.; Sandifer, Carl E.

    2011-01-01

    Causal factors in aviation accidents and incidents related to system/component failure/malfunction (SCFM) were examined for Federal Aviation Regulation Parts 121 and 135 operations to establish future requirements for the NASA Aviation Safety Program s Integrated Vehicle Health Management (IVHM) Project. Data analyzed includes National Transportation Safety Board (NSTB) accident data (1988 to 2003), Federal Aviation Administration (FAA) incident data (1988 to 2003), and Aviation Safety Reporting System (ASRS) incident data (1993 to 2008). Failure modes and effects analyses were examined to identify possible modes of SCFM. A table of potential adverse conditions was developed to help evaluate IVHM research technologies. Tables present details of specific SCFM for the incidents and accidents. Of the 370 NTSB accidents affected by SCFM, 48 percent involved the engine or fuel system, and 31 percent involved landing gear or hydraulic failure and malfunctions. A total of 35 percent of all SCFM accidents were caused by improper maintenance. Of the 7732 FAA database incidents affected by SCFM, 33 percent involved landing gear or hydraulics, and 33 percent involved the engine and fuel system. The most frequent SCFM found in ASRS were turbine engine, pressurization system, hydraulic main system, flight management system/flight management computer, and engine. Because the IVHM Project does not address maintenance issues, and landing gear and hydraulic systems accidents are usually not fatal, the focus of research should be those SCFMs that occur in the engine/fuel and flight control/structures systems as well as power systems.

  10. Device-independent test of causal order and relations to fixed-points

    NASA Astrophysics Data System (ADS)

    Baumeler, Ämin; Wolf, Stefan

    2016-03-01

    Bell non-local correlations cannot be naturally explained in a fixed causal structure. This serves as a motivation for considering models where no global assumption is made beyond logical consistency. The assumption of a fixed causal order between a set of parties, together with free randomness, implies device-independent inequalities—just as the assumption of locality does. It is known that local validity of quantum theory is consistent with violating such inequalities. Moreover, for three parties or more, even the (stronger) assumption of local classical probability theory plus logical consistency allows for violating causal inequalities. Here, we show that a classical environment (with which the parties interact), possibly containing loops, is logically consistent if and only if whatever the involved parties do, there is exactly one fixed-point, the latter being representable as a mixture of deterministic fixed-points. We further show that the non-causal view allows for a model of computation strictly more powerful than computation in a world of fixed causal orders.

  11. 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.

  12. [Information sources for causality assessment of health problems related to health foods and their usefulness].

    PubMed

    Umegaki, Keizo; Yamada, Hiroshi; Chiba, Tsuyoshi; Nakanishi, Tomoko; Sato, Yoko; Fukuyama, Satoshi

    2013-01-01

    Collecting adverse case reports suspected to be due to health foods and evaluation of the causality are important to secure safety, even if the causal relationship between health foods and reported health problem is uncertain. Case reports are mainly collected at three sites: public health centers, practical living information online network system(PIO-NET), and individual companies. The case reports from the three sources are not dealt with consistently. In this study, we investigated and characterized those case reports from the viewpoint of evaluating causality, using the causality association rating methods, namely, the dendritic and pointed methods, which we reported previously. Information in public health centers comprised 20 reports per year; approximately 40% were from health care providers and contained detailed medical data. PIO-NET information comprised 366 reports per year; 80% were self-reports from users, and few medical details were included. Company information covered 1,323 cases from 13 companies; more than 90% were from users and most of them were complaints. Case reports from public health centers and PIO-NET showed that the largerst number of victims were female aged >60, with allergy and gastrointestinal symptoms. When these case reports from the letter two sources were examined using the causality association rating systems, most were rated as "possible" and only a few were rated as "probable". As specific case reports from different information sources were examined in this study, we were able to identify several points that should be improved in our two rating methods. However, to ensure the safety of health foods, it will be necessary to collect a large number of high-quality case reports for evaluation by a suitable causality rating method, and to integrate those evaluated case reports into a single site.

  13. Long-Range Temporal Correlations, Multifractality, and the Causal Relation between Neural Inputs and Movements

    PubMed Central

    Hu, Jing; Zheng, Yi; Gao, Jianbo

    2013-01-01

    Understanding the causal relation between neural inputs and movements is very important for the success of brain-machine interfaces (BMIs). In this study, we analyze 104 neurons’ firings using statistical, information theoretic, and fractal analysis. The latter include Fano factor analysis, multifractal adaptive fractal analysis (MF-AFA), and wavelet multifractal analysis. We find neuronal firings are highly non-stationary, and Fano factor analysis always indicates long-range correlations in neuronal firings, irrespective of whether those firings are correlated with movement trajectory or not, and thus does not reveal any actual correlations between neural inputs and movements. On the other hand, MF-AFA and wavelet multifractal analysis clearly indicate that when neuronal firings are not well correlated with movement trajectory, they do not have or only have weak temporal correlations. When neuronal firings are well correlated with movements, they are characterized by very strong temporal correlations, up to a time scale comparable to the average time between two successive reaching tasks. This suggests that neurons well correlated with hand trajectory experienced a “re-setting” effect at the start of each reaching task, in the sense that within the movement correlated neurons the spike trains’ long-range dependences persisted about the length of time the monkey used to switch between task executions. A new task execution re-sets their activity, making them only weakly correlated with their prior activities on longer time scales. We further discuss the significance of the coalition of those important neurons in executing cortical control of prostheses. PMID:24130549

  14. 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…

  15. 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.

  16. Embeddings of Causal Sets

    SciTech Connect

    Reid, David D.

    2009-07-06

    A key postulate of the causal set program is that this discrete partial order offers a sufficiently rich structure to make it a viable model of spacetime for quantum gravity. If the deep structure of spacetime is that of a causal set, then the correspondence principle (with the spacetimes of general relativity) must be obeyed. Therefore, one of the requirements of this program is to establish that the causal set structure is in fact, not just in principle, fully consistent with our macroscopic notion of spacetime as a Lorentzian manifold. An important component of any such 'manifold test' is the ability to find embeddings of causal sets into Lorentzian manifolds.

  17. Causal networks or causal islands? The representation of mechanisms and the transitivity of causal judgment

    PubMed Central

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

    2014-01-01

    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 mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we used causal transitivity—the inference 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 4–5). PMID:25556901

  18. Causal determination of acoustic group velocity and frequency derivative of attenuation with finite-bandwidth Kramers-Kronig relations

    NASA Astrophysics Data System (ADS)

    Mobley, Joel; Waters, Kendall R.; Miller, James G.

    2005-07-01

    Kramers-Kronig (KK) analyses of experimental data are complicated by the extrapolation problem, that is, how the unexamined spectral bands impact KK calculations. This work demonstrates the causal linkages in resonant-type data provided by acoustic KK relations for the group velocity (cg) and the derivative of the attenuation coefficient (α') (components of the derivative of the acoustic complex wave number) without extrapolation or unmeasured parameters. These relations provide stricter tests of causal consistency relative to previously established KK relations for the phase velocity (cp) and attenuation coefficient (α) (components of the undifferentiated acoustic wave number) due to their shape invariance with respect to subtraction constants. For both the group velocity and attenuation derivative, three forms of the relations are derived. These relations are equivalent for bandwidths covering the entire infinite spectrum, but differ when restricted to bandlimited spectra. Using experimental data from suspensions of elastic spheres in saline, the accuracy of finite-bandwidth KK predictions for cg and α' is demonstrated. Of the multiple methods, the most accurate were found to be those whose integrals were expressed only in terms of the phase velocity and attenuation coefficient themselves, requiring no differentiated quantities.

  19. Causal determination of acoustic group velocity and frequency derivative of attenuation with finite-bandwidth Kramers-Kronig relations.

    PubMed

    Mobley, Joel; Waters, Kendall R; Miller, James G

    2005-07-01

    Kramers-Kronig (KK) analyses of experimental data are complicated by the extrapolation problem, that is, how the unexamined spectral bands impact KK calculations. This work demonstrates the causal linkages in resonant-type data provided by acoustic KK relations for the group velocity (c(g)) and the derivative of the attenuation coefficient (alpha') (components of the derivative of the acoustic complex wave number) without extrapolation or unmeasured parameters. These relations provide stricter tests of causal consistency relative to previously established KK relations for the phase velocity (c(p)) and attenuation coefficient (alpha) (components of the undifferentiated acoustic wave number) due to their shape invariance with respect to subtraction constants. For both the group velocity and attenuation derivative, three forms of the relations are derived. These relations are equivalent for bandwidths covering the entire infinite spectrum, but differ when restricted to bandlimited spectra. Using experimental data from suspensions of elastic spheres in saline, the accuracy of finite-bandwidth KK predictions for c(g) and alpha' is demonstrated. Of the multiple methods, the most accurate were found to be those whose integrals were expressed only in terms of the phase velocity and attenuation coefficient themselves, requiring no differentiated quantities.

  20. Increased Causal Connectivity Related to Anatomical Alterations as Potential Endophenotypes for Schizophrenia

    PubMed Central

    Guo, Wenbin; Liu, Feng; Xiao, Changqing; Yu, Miaoyu; Zhang, Zhikun; Liu, Jianrong; Zhang, Jian; Zhao, Jingping

    2015-01-01

    Abstract Anatomical and functional abnormalities in the cortico-cerebellar-thalamo-cortical circuit have been observed in schizophrenia patients and their unaffected siblings. However, it remains unclear to the relationship between anatomical and functional abnormalities within this circuit in schizophrenia patients and their unaffected siblings, which may serve as potential endophenotypes for schizophrenia. Anatomical and resting-state functional magnetic resonance imaging data were acquired from 49 first-episode, drug-naive schizophrenia patients, 46 unaffected siblings, and 46 healthy controls. Data were analyzed by using voxel-based morphometry and Granger causality analysis. The patients and the siblings shared anatomical deficits in the left middle temporal gyrus (MTG) and increased left MTG–left angular gyrus (AG) connectivity. Moreover, the left MTG–left AG connectivity negatively correlates to the duration of untreated psychosis in the patients. The findings indicate that anatomical deficits in the left MTG and its increased causal connectivity with the left AG may serve as potential endophenotypes for schizophrenia with clinical implications. PMID:26496253

  1. Assessing Interactive Causal Influence

    ERIC Educational Resources Information Center

    Novick, Laura R.; Cheng, Patricia W.

    2004-01-01

    The discovery of conjunctive causes--factors that act in concert to produce or prevent an effect--has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses…

  2. Circadian Rhythms and Mood Disorders: Are the Phenomena and Mechanisms Causally Related?

    PubMed Central

    Bechtel, William

    2015-01-01

    This paper reviews some of the compelling evidence of disrupted circadian rhythms in individuals with mood disorders (major depressive disorder, seasonal affective disorder, and bipolar disorder) and that treatments such as bright light, designed to alter circadian rhythms, are effective in treating these disorders. Neurotransmitters in brain regions implicated in mood regulation exhibit circadian rhythms. A mouse model originally employed to identify a circadian gene has proven a potent model for mania. While this evidence is suggestive of an etiological role for altered circadian rhythms in mood disorders, it is compatible with other explanations, including that disrupted circadian rhythms and mood disorders are effects of a common cause and that genes and proteins implicated in both simply have pleiotropic effects. In light of this, the paper advances a proposal as to what evidence would be needed to establish a direct causal link between disruption of circadian rhythms and mood disorders. PMID:26379559

  3. Dynamics of large-scale cortical interactions at high gamma frequencies during word production: event related causality (ERC) analysis of human electrocorticography (ECoG).

    PubMed

    Korzeniewska, Anna; Franaszczuk, Piotr J; Crainiceanu, Ciprian M; Kuś, Rafał; Crone, Nathan E

    2011-06-15

    Intracranial EEG studies in humans have shown that functional brain activation in a variety of functional-anatomic domains of human cortex is associated with an increase in power at a broad range of high gamma (>60Hz) frequencies. Although these electrophysiological responses are highly specific for the location and timing of cortical processing and in animal recordings are highly correlated with increased population firing rates, there has been little direct empirical evidence for causal interactions between different recording sites at high gamma frequencies. Such causal interactions are hypothesized to occur during cognitive tasks that activate multiple brain regions. To determine whether such causal interactions occur at high gamma frequencies and to investigate their functional significance, we used event-related causality (ERC) analysis to estimate the dynamics, directionality, and magnitude of event-related causal interactions using subdural electrocorticography (ECoG) recorded during two word production tasks: picture naming and auditory word repetition. A clinical subject who had normal hearing but was skilled in American Signed Language (ASL) provided a unique opportunity to test our hypothesis with reference to a predictable pattern of causal interactions, i.e. that language cortex interacts with different areas of sensorimotor cortex during spoken vs. signed responses. Our ERC analyses confirmed this prediction. During word production with spoken responses, perisylvian language sites had prominent causal interactions with mouth/tongue areas of motor cortex, and when responses were gestured in sign language, the most prominent interactions involved hand and arm areas of motor cortex. Furthermore, we found that the sites from which the most numerous and prominent causal interactions originated, i.e. sites with a pattern of ERC "divergence", were also sites where high gamma power increases were most prominent and where electrocortical stimulation mapping

  4. Significant increase in trisomy 21 in Berlin nine months after the Chernobyl reactor accident: temporal correlation or causal relation?

    PubMed Central

    Sperling, K.; Pelz, J.; Wegner, R. D.; Dörries, A.; Grüters, A.; Mikkelsen, M.

    1994-01-01

    OBJECTIVE--To assess whether the increased prevalence of trisomy 21 in West Berlin in January 1987 might have been causally related to exposure to ionising radiation as a result of the Chernobyl reactor accident or was merely a chance event. DESIGN--Analysis of monthly prevalence of trisomy 21 in West Berlin from January 1980 to December 1989. SETTING--Confines of West Berlin. RESULTS--Owing to the former "island" situation of West Berlin and its well organised health services, ascertainment of trisomy 21 was thought to be almost complete. A cluster of 12 cases occurred in January 1987 as compared with two or three expected. After exclusion of factors that might have explained the increase, including maternal age distribution, only exposure to radiation as a result of the Chernobyl reactor accident remained. In six of seven cases that could be studied cytogenetically the extra chromosome was of maternal origin, confirming that nondisjunction had occurred at about the time of conception. CONCLUSION--On the basis of two assumptions--(a) that maternal meiosis is an error prone process susceptible to exogenous factors at the time of conception; (b) that owing to the high prevalence of iodine deficiency in Berlin a large amount of iodine-131 would have been accumulated over a short period--it is concluded that the increased prevalence of trisomy 21 in West Berlin in January 1987 was causally related to a short period of exposure to ionising radiation as a result of the Chernobyl reactor accident. PMID:8044094

  5. Causal Factors and Adverse Conditions of Aviation Accidents and Incidents Related to Integrated Resilient Aircraft Control

    NASA Technical Reports Server (NTRS)

    Reveley, Mary S.; Briggs, Jeffrey L.; Evans, Joni K.; Sandifer, Carl E.; Jones, Sharon Monica

    2010-01-01

    The causal factors of accidents from the National Transportation Safety Board (NTSB) database and incidents from the Federal Aviation Administration (FAA) database associated with loss of control (LOC) were examined for four types of operations (i.e., Federal Aviation Regulation Part 121, Part 135 Scheduled, Part 135 Nonscheduled, and Part 91) for the years 1988 to 2004. In-flight LOC is a serious aviation problem. Well over half of the LOC accidents included at least one fatality (80 percent in Part 121), and roughly half of all aviation fatalities in the studied time period occurred in conjunction with LOC. An adverse events table was updated to provide focus to the technology validation strategy of the Integrated Resilient Aircraft Control (IRAC) Project. The table contains three types of adverse conditions: failure, damage, and upset. Thirteen different adverse condition subtypes were gleaned from the Aviation Safety Reporting System (ASRS), the FAA Accident and Incident database, and the NTSB database. The severity and frequency of the damage conditions, initial test conditions, and milestones references are also provided.

  6. Is socioeconomic status of the rearing environment causally related to obesity in the offspring?

    PubMed

    Fontaine, Kevin R; Robertson, Henry T; Holst, Claus; Desmond, Renee; Stunkard, Albert J; Sørensen, Thorkild I A; Allison, David B

    2011-01-01

    We attempt to elucidate whether there might be a causal connection between the socioeconomic status (SES) of the rearing environment and obesity in the offspring using data from two large-scale adoption studies: (1) The Copenhagen Adoption Study of Obesity (CASO), and (2) The Survey of Holt Adoptees and Their Families (HOLT). In CASO, the SES of both biological and adoptive parents was known, but all children were adopted. In HOLT, only the SES of the rearing parents was known, but the children could be either biological or adopted. After controlling for relevant covariates (e.g., adoptee age at measurement, adoptee age at transfer, adoptee sex) the raw (unstandardized) regression coefficients for adoptive and biological paternal SES on adoptee body mass index (BMI: kg/m(2)) in CASO were -.22 and -.23, respectively, both statistically significant (p = 0.01). Controlling for parental BMI (both adoptive and biological) reduced the coefficient for biological paternal SES by 44% (p = .034) and the coefficient for adoptive paternal SES by 1%. For HOLT, the regression coefficients for rearing parent SES were -.42 and -.25 for biological and adoptive children, respectively. Controlling for the average BMI of the rearing father and mother (i.e., mid-parental BMI) reduced the SES coefficient by 47% in their biological offspring (p≤.0001), and by 12% in their adoptive offspring (p = .09). Thus, despite the differing structures of the two adoption studies, both suggest that shared genetic diathesis and direct environmental transmission contribute about equally to the association between rearing SES and offspring BMI. PMID:22110724

  7. Is There a Causal Relation between Maternal Acetaminophen Administration and ADHD?

    PubMed Central

    Saad, Antonio; Hegde, Shruti; Kechichian, Talar; Gamble, Phyllis; Rahman, Mahbubur; Stutz, Sonja J.; Anastasio, Noelle C.; Alshehri, Wael; Lei, Jun; Mori, Susumu; Kajs, Bridget; Cunningham, Kathryn A.; Saade, George; Burd, Irina; Costantine, Maged

    2016-01-01

    Objective Recent epidemiological studies reported an association between maternal intake of acetaminophen (APAP) and attention deficit hyperactivity disorder (ADHD) in their children. However, none of these studies demonstrated causality. Our objective was to determine whether exposure to APAP during pregnancy result in hyperkinetic dysfunctions in offspring, using a murine model. Material and Methods Pregnant CD1 mice (N = 8/group) were allocated to receive by gavage either APAP (150 mg/kg/day, equivalent to the FDA-approved maximum human clinical dose), or 0.5% carboxymethylcellulose (control group), starting on embryonic day 7 until delivery. Maternal serum APAP and alanine transaminase (ALT) concentrations were determined by ELISA and kinetic colorimetric assays, respectively. Open field locomotor activity (LMA) in the 30-day old mouse offspring was quantified using Photobeam Activity System. Mouse offspring were then sacrificed, whole brains processed for magnetic resonance imaging (MRI; 11.7 Tesla magnet) and for neuronal quantification using Nissl stain. The association between APAP exposure and LMA in mouse offspring was analyzed using a mixed effects Poisson regression model that accounted for mouse offspring weight, gender, random selection, and testing time and day. We corrected for multiple comparisons and considered P<0.008 as statistically significant. Results Maternal serum APAP concentration peaked 30 minutes after gavage, reaching the expected mean of 117 μg/ml. Serum ALT concentrations were not different between groups. There were no significant differences in vertical (rearing), horizontal, or total locomotor activity between the two rodent offspring groups at the P level fixed to adjust for multiple testing. In addition, no differences were found in volumes of 29 brain areas of interest on MRI or in neuronal quantifications between the two groups. Conclusion This study refutes that hypothesis that prenatal exposure to APAP causes hyperkinetic

  8. An application of GIS and Bayesian network in studying spatial-causal relations between enterprises and environmental factors

    NASA Astrophysics Data System (ADS)

    Shen, Tiyan; Li, Xi; Li, Maiqing

    2009-10-01

    The paper intends to employ Geographic Information System (GIS) and Bayesian Network to discover the spatial causality between enterprises and environmental factors in Beijing Metropolis. The census data of Beijing was spatialized by means of GIS in the beginning, and then the training data was made using density mapping technique. Base on the training data, the structure of a Bayesian Network was learnt with the help of Maximum Weight Spanning Tree. Eight direct relations were discussed in the end, of which, the most exciting discovery, "Enterprise-Run Society", as the symbol of the former planned economy, was emphasized in the spatial relations between heavy industry and schools. Though the final result is not so creative in economic perspective, it is of significance in technique view due to all discoveries were drawn from data, therefore leading to the realization of the importance of GIS and data mining to economic geography research.

  9. Human causal discovery from observational data.

    PubMed Central

    Hashem, A. I.; Cooper, G. F.

    1996-01-01

    Utilizing Bayesian belief networks as a model of causality, we examined medical students' ability to discover causal relationships from observational data. Nine sets of patient cases were generated from relatively simple causal belief networks by stochastic simulation. Twenty participants examined the data sets and attempted to discover the underlying causal relationships. Performance was poor in general, except at discovering the absence of a causal relationship. This work supports the potential for combining human and computer methods for causal discovery. PMID:8947621

  10. Warp drive and causality

    NASA Astrophysics Data System (ADS)

    Everett, Allen E.

    1996-06-01

    Alcubierre recently exhibited a spacetime which, within the framework of general relativity, allows travel at superluminal speeds if matter with a negative energy density can exist, and conjectured that it should be possible to use similar techniques to construct a theory containing closed causal loops and, thus, travel backwards in time. We verify this conjecture by exhibiting a simple modification of Alcubierre's model, requiring no additional assumptions, in which causal loops are possible. We also note that this mechanism for generating causal loops differs in essential ways from that discovered by Gott involving cosmic strings.

  11. 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.

  12. Anterior Cingulate Cortico-Hippocampal Dysconnectivity in Unaffected Relatives of Schizophrenia Patients: A Stochastic Dynamic Causal Modeling Study.

    PubMed

    Xi, Yi-Bin; Li, Chen; Cui, Long-Biao; Liu, Jian; Guo, Fan; Li, Liang; Liu, Ting-Ting; Liu, Kang; Chen, Gang; Xi, Min; Wang, Hua-Ning; Yin, Hong

    2016-01-01

    Familial risk plays a significant role in the etiology of schizophrenia (SZ). Many studies using neuroimaging have demonstrated structural and functional alterations in relatives of SZ patients, with significant results found in diverse brain regions involving the anterior cingulate cortex (ACC), caudate, dorsolateral prefrontal cortex (DLPFC), and hippocampus. This study investigated whether unaffected relatives of first episode SZ differ from healthy controls (HCs) in effective connectivity measures among these regions. Forty-six unaffected first-degree relatives of first episode SZ patients-according to the DSM-IV-were studied. Fifty HCs were included for comparison. All subjects underwent resting state functional magnetic resonance imaging (fMRI). We used stochastic dynamic causal modeling (sDCM) to estimate the directed connections between the left ACC, right ACC, left caudate, right caudate, left DLPFC, left hippocampus, and right hippocampus. We used Bayesian parameter averaging (BPA) to characterize the differences. The BPA results showed hyperconnectivity from the left ACC to right hippocampus and hypoconnectivity from the right ACC to right hippocampus in SZ relatives compared to HCs. The pattern of anterior cingulate cortico-hippocampal connectivity in SZ relatives may be a familial feature of SZ risk, appearing to reflect familial susceptibility for SZ. PMID:27512370

  13. Anterior Cingulate Cortico-Hippocampal Dysconnectivity in Unaffected Relatives of Schizophrenia Patients: A Stochastic Dynamic Causal Modeling Study

    PubMed Central

    Xi, Yi-Bin; Li, Chen; Cui, Long-Biao; Liu, Jian; Guo, Fan; Li, Liang; Liu, Ting-Ting; Liu, Kang; Chen, Gang; Xi, Min; Wang, Hua-Ning; Yin, Hong

    2016-01-01

    Familial risk plays a significant role in the etiology of schizophrenia (SZ). Many studies using neuroimaging have demonstrated structural and functional alterations in relatives of SZ patients, with significant results found in diverse brain regions involving the anterior cingulate cortex (ACC), caudate, dorsolateral prefrontal cortex (DLPFC), and hippocampus. This study investigated whether unaffected relatives of first episode SZ differ from healthy controls (HCs) in effective connectivity measures among these regions. Forty-six unaffected first-degree relatives of first episode SZ patients—according to the DSM-IV—were studied. Fifty HCs were included for comparison. All subjects underwent resting state functional magnetic resonance imaging (fMRI). We used stochastic dynamic causal modeling (sDCM) to estimate the directed connections between the left ACC, right ACC, left caudate, right caudate, left DLPFC, left hippocampus, and right hippocampus. We used Bayesian parameter averaging (BPA) to characterize the differences. The BPA results showed hyperconnectivity from the left ACC to right hippocampus and hypoconnectivity from the right ACC to right hippocampus in SZ relatives compared to HCs. The pattern of anterior cingulate cortico-hippocampal connectivity in SZ relatives may be a familial feature of SZ risk, appearing to reflect familial susceptibility for SZ. PMID:27512370

  14. PPInterFinder--a mining tool for extracting causal relations on human proteins from literature.

    PubMed

    Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar

    2013-01-01

    One of the most common and challenging problem in biomedical text mining is to mine protein-protein interactions (PPIs) from MEDLINE abstracts and full-text research articles because PPIs play a major role in understanding the various biological processes and the impact of proteins in diseases. We implemented, PPInterFinder--a web-based text mining tool to extract human PPIs from biomedical literature. PPInterFinder uses relation keyword co-occurrences with protein names to extract information on PPIs from MEDLINE abstracts and consists of three phases. First, it identifies the relation keyword using a parser with Tregex and a relation keyword dictionary. Next, it automatically identifies the candidate PPI pairs with a set of rules related to PPI recognition. Finally, it extracts the relations by matching the sentence with a set of 11 specific patterns based on the syntactic nature of PPI pair. We find that PPInterFinder is capable of predicting PPIs with the accuracy of 66.05% on AIMED corpus and outperforms most of the existing systems. DATABASE URL: http://www.biomining-bu.in/ppinterfinder/ PMID:23325628

  15. PPInterFinder--a mining tool for extracting causal relations on human proteins from literature.

    PubMed

    Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar

    2013-01-01

    One of the most common and challenging problem in biomedical text mining is to mine protein-protein interactions (PPIs) from MEDLINE abstracts and full-text research articles because PPIs play a major role in understanding the various biological processes and the impact of proteins in diseases. We implemented, PPInterFinder--a web-based text mining tool to extract human PPIs from biomedical literature. PPInterFinder uses relation keyword co-occurrences with protein names to extract information on PPIs from MEDLINE abstracts and consists of three phases. First, it identifies the relation keyword using a parser with Tregex and a relation keyword dictionary. Next, it automatically identifies the candidate PPI pairs with a set of rules related to PPI recognition. Finally, it extracts the relations by matching the sentence with a set of 11 specific patterns based on the syntactic nature of PPI pair. We find that PPInterFinder is capable of predicting PPIs with the accuracy of 66.05% on AIMED corpus and outperforms most of the existing systems. DATABASE URL: http://www.biomining-bu.in/ppinterfinder/

  16. 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.

  17. Causal inference based on counterfactuals

    PubMed Central

    Höfler, M

    2005-01-01

    Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept. PMID:16159397

  18. 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.…

  19. Sensing the Coherence of Biology in Contrast to Psychology: Young Children's Use of Causal Relations to Distinguish Two Foundational Domains

    ERIC Educational Resources Information Center

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

    2010-01-01

    To what extent do children understand that biological processes fall into 1 coherent domain unified by distinct causal principles? In Experiments 1 and 2 (N = 125) kindergartners are given triads of biological and psychological processes and asked to identify which 2 members of the triad belong together. Results show that 5-year-olds correctly…

  20. Apparent causality affects perceived simultaneity.

    PubMed

    Kohlrausch, Armin; van Eijk, Rob; Juola, James F; Brandt, Inge; van de Par, Steven

    2013-10-01

    The present research addresses the question of how visual predictive information and implied causality affect audio-visual synchrony perception. Previous research has shown a systematic shift in the likelihood of observers to accept audio-leading stimulus pairs as being apparently simultaneous in variants of audio-visual stimulus pairs that differ in (1) the amount of visual predictive information available and (2) the apparent causal relation between the auditory and visual components. An experiment was designed to separate the predictability and causality explanations, and the results indicated that shifts in subjective simultaneity were explained completely by changes in the implied causal relations in the stimuli and that predictability had no added value. Together with earlier findings, these results further indicate that the observed shifts in subjective simultaneity due to causal relations among auditory and visual events do not reflect a mere change in response strategy, but rather result from early multimodal integration processes in event perception.

  1. Causality in epidemiology.

    PubMed

    Kamangar, Farin

    2012-10-01

    This article provides an introduction to the meaning of causality in epidemiology and methods that epidemiologists use to distinguish causal associations from non-causal ones. Alternatives to causal association are discussed in detail. Hill's guidelines, set forth approximately 50 years ago, and more recent developments are reviewed. The role of religious and philosophic views in our understanding of causality is briefly discussed.

  2. 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. PMID:23098315

  3. On causality of extreme events

    PubMed Central

    2016-01-01

    Multiple metrics have been developed to detect causality relations between data describing the elements constituting complex systems, all of them considering their evolution through time. Here we propose a metric able to detect causality within static data sets, by analysing how extreme events in one element correspond to the appearance of extreme events in a second one. The metric is able to detect non-linear causalities; to analyse both cross-sectional and longitudinal data sets; and to discriminate between real causalities and correlations caused by confounding factors. We validate the metric through synthetic data, dynamical and chaotic systems, and data representing the human brain activity in a cognitive task. We further show how the proposed metric is able to outperform classical causality metrics, provided non-linear relationships are present and large enough data sets are available. PMID:27330866

  4. On causality of extreme events.

    PubMed

    Zanin, Massimiliano

    2016-01-01

    Multiple metrics have been developed to detect causality relations between data describing the elements constituting complex systems, all of them considering their evolution through time. Here we propose a metric able to detect causality within static data sets, by analysing how extreme events in one element correspond to the appearance of extreme events in a second one. The metric is able to detect non-linear causalities; to analyse both cross-sectional and longitudinal data sets; and to discriminate between real causalities and correlations caused by confounding factors. We validate the metric through synthetic data, dynamical and chaotic systems, and data representing the human brain activity in a cognitive task. We further show how the proposed metric is able to outperform classical causality metrics, provided non-linear relationships are present and large enough data sets are available.

  5. Circular causality.

    PubMed

    Thomas, R

    2006-07-01

    The problem of disentangling complex dynamic systems is addressed, especially with a view to identifying those variables that take part in the essential qualitative behaviour of systems. The author presents a series of reflections about the methods of formalisation together with the principles that govern the global operation of systems. In particular, a section on circuits, nuclei, and circular causality and a rather detailed description of the analytic use of the generalised asynchronous logical description, together with a brief description of its synthetic use (OreverseO logic). Some basic rules are recalled, such as the fact that a positive circuit is a necessary condition of multistationarity. Also, the interest of considering as a model, rather than a well-defined set of differential equations, a variety of systems that differ from each other only by the values of constant terms is emphasised. All these systems have a common Jacobian matrix and for all of them phase space has exactly the same structure. It means that all can be partitioned in the same way as regards the signs of the eigenvalues and thus as regards the precise nature of any steady states that might be present. Which steady states are actually present, depends on the values of terms of order zero in the ordinary differential equations (ODEs), and it is easy to find for which values of these terms a given point in phase space is steady. Models can be synthesised first at the level of the circuits involved in the Jacobian matrix (that determines which types and numbers of steady states are consistent with the model), then only at the level of terms of order zero in the ODE's (that determines which of the steady states actually exist), hence the title 'Circular casuality'.

  6. The Visual Causality Analyst: An Interactive Interface for Causal Reasoning.

    PubMed

    Wang, Jun; Mueller, Klaus

    2016-01-01

    Uncovering the causal relations that exist among variables in multivariate datasets is one of the ultimate goals in data analytics. Causation is related to correlation but correlation does not imply causation. While a number of casual discovery algorithms have been devised that eliminate spurious correlations from a network, there are no guarantees that all of the inferred causations are indeed true. Hence, bringing a domain expert into the casual reasoning loop can be of great benefit in identifying erroneous casual relationships suggested by the discovery algorithm. To address this need we present the Visual Causal Analyst-a novel visual causal reasoning framework that allows users to apply their expertise, verify and edit causal links, and collaborate with the causal discovery algorithm to identify a valid causal network. Its interface consists of both an interactive 2D graph view and a numerical presentation of salient statistical parameters, such as regression coefficients, p-values, and others. Both help users in gaining a good understanding of the landscape of causal structures particularly when the number of variables is large. Our framework is also novel in that it can handle both numerical and categorical variables within one unified model and return plausible results. We demonstrate its use via a set of case studies using multiple practical datasets.

  7. Quantum causal modelling

    NASA Astrophysics Data System (ADS)

    Costa, Fabio; Shrapnel, Sally

    2016-06-01

    Causal modelling provides a powerful set of tools for identifying causal structure from observed correlations. It is well known that such techniques fail for quantum systems, unless one introduces ‘spooky’ hidden mechanisms. Whether one can produce a genuinely quantum framework in order to discover causal structure remains an open question. Here we introduce a new framework for quantum causal modelling that allows for the discovery of causal structure. We define quantum analogues for core features of classical causal modelling techniques, including the causal Markov condition and faithfulness. Based on the process matrix formalism, this framework naturally extends to generalised structures with indefinite causal order.

  8. Improving causality induction with category learning.

    PubMed

    Guo, Yi; Wang, Zhihong; Shao, Zhiqing

    2014-01-01

    Causal relations are of fundamental importance for human perception and reasoning. According to the nature of causality, causality has explicit and implicit forms. In the case of explicit form, causal-effect relations exist at either clausal or discourse levels. The implicit causal-effect relations heavily rely on empirical analysis and evidence accumulation. This paper proposes a comprehensive causality extraction system (CL-CIS) integrated with the means of category-learning. CL-CIS considers cause-effect relations in both explicit and implicit forms and especially practices the relation between category and causality in computation. In elaborately designed experiments, CL-CIS is evaluated together with general causality analysis system (GCAS) and general causality analysis system with learning (GCAS-L), and it testified to its own capability and performance in construction of cause-effect relations. This paper confirms the expectation that the precision and coverage of causality induction can be remarkably improved by means of causal and category learning.

  9. Improving Causality Induction with Category Learning

    PubMed Central

    Wang, Zhihong; Shao, Zhiqing

    2014-01-01

    Causal relations are of fundamental importance for human perception and reasoning. According to the nature of causality, causality has explicit and implicit forms. In the case of explicit form, causal-effect relations exist at either clausal or discourse levels. The implicit causal-effect relations heavily rely on empirical analysis and evidence accumulation. This paper proposes a comprehensive causality extraction system (CL-CIS) integrated with the means of category-learning. CL-CIS considers cause-effect relations in both explicit and implicit forms and especially practices the relation between category and causality in computation. In elaborately designed experiments, CL-CIS is evaluated together with general causality analysis system (GCAS) and general causality analysis system with learning (GCAS-L), and it testified to its own capability and performance in construction of cause-effect relations. This paper confirms the expectation that the precision and coverage of causality induction can be remarkably improved by means of causal and category learning. PMID:24883419

  10. Effect of measurement noise on Granger causality.

    PubMed

    Nalatore, Hariharan; Sasikumar, N; Rangarajan, Govindan

    2014-12-01

    Most of the signals recorded in experiments are inevitably contaminated by measurement noise. Hence, it is important to understand the effect of such noise on estimating causal relations between such signals. A primary tool for estimating causality is Granger causality. Granger causality can be computed by modeling the signal using a bivariate autoregressive (AR) process. In this paper, we greatly extend the previous analysis of the effect of noise by considering a bivariate AR process of general order p. From this analysis, we analytically obtain the dependence of Granger causality on various noise-dependent system parameters. In particular, we show that measurement noise can lead to spurious Granger causality and can suppress true Granger causality. These results are verified numerically. Finally, we show how true causality can be recovered numerically using the Kalman expectation maximization algorithm.

  11. Cross-lagged relations between mentoring received from supervisors and employee OCBs: Disentangling causal direction and identifying boundary conditions.

    PubMed

    Eby, Lillian T; Butts, Marcus M; Hoffman, Brian J; Sauer, Julia B

    2015-07-01

    Although mentoring has documented relationships with employee attitudes and outcomes of interest to organizations, neither the causal direction nor boundary conditions of the relationship between mentoring and organizational citizenship behaviors (OCBs) has been fully explored. On the basis of Social Learning Theory (SLT; Bandura, 1977, 1986), we predicted that mentoring received by supervisors would causally precede OCBs, rather than employee OCBs resulting in the receipt of more mentoring from supervisors. Results from cross-lagged data collected at 2 points in time from 190 intact supervisor-employee dyads supported our predictions; however, only for OCBs directed at individuals (OCB-Is) and not for OCBs directed at the organization (OCB-Os). Further supporting our theoretical rationale for expecting mentoring to precede OCBs, we found that coworker support operates as a substitute for mentoring in predicting OCB-Is. By contrast, no moderating effects were found for perceived organizational support. The results are discussed in terms of theoretical implications for mentoring and OCB research, as well as practical suggestions for enhancing employee citizenship behaviors.

  12. Causally nonseparable processes admitting a causal model

    NASA Astrophysics Data System (ADS)

    Feix, Adrien; Araújo, Mateus; Brukner, Časlav

    2016-08-01

    A recent framework of quantum theory with no global causal order predicts the existence of ‘causally nonseparable’ processes. Some of these processes produce correlations incompatible with any causal order (they violate so-called ‘causal inequalities’ analogous to Bell inequalities) while others do not (they admit a ‘causal model’ analogous to a local model). Here we show for the first time that bipartite causally nonseparable processes with a causal model exist, and give evidence that they have no clear physical interpretation. We also provide an algorithm to generate processes of this kind and show that they have nonzero measure in the set of all processes. We demonstrate the existence of processes which stop violating causal inequalities but are still causally nonseparable when mixed with a certain amount of ‘white noise’. This is reminiscent of the behavior of Werner states in the context of entanglement and nonlocality. Finally, we provide numerical evidence for the existence of causally nonseparable processes which have a causal model even when extended with an entangled state shared among the parties.

  13. 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

  14. Detecting causality in complex ecosystems.

    PubMed

    Sugihara, George; May, Robert; Ye, Hao; Hsieh, Chih-hao; Deyle, Ethan; Fogarty, Michael; Munch, Stephan

    2012-10-26

    Identifying causal networks is important for effective policy and management recommendations on climate, epidemiology, financial regulation, and much else. We introduce a method, based on nonlinear state space reconstruction, that can distinguish causality from correlation. It extends to nonseparable weakly connected dynamic systems (cases not covered by the current Granger causality paradigm). The approach is illustrated both by simple models (where, in contrast to the real world, we know the underlying equations/relations and so can check the validity of our method) and by application to real ecological systems, including the controversial sardine-anchovy-temperature problem.

  15. The Power of Causal Beliefs and Conflicting Evidence on Causal Judgments and Decision Making

    ERIC Educational Resources Information Center

    Garcia-Retamero, Rocio; Muller, Stephanie M.; Catena, Andres; Maldonado, Antonio

    2009-01-01

    In two experiments, we investigated the relative impact of causal beliefs and empirical evidence on both decision making and causal judgments, and whether this relative impact could be altered by previous experience. 2. Selected groups of participants in both experiments received pre-training with either causal or neutral cues, or no pre-training…

  16. Attitudes toward Others Depend upon Self and Other Causal Uncertainty

    PubMed Central

    Tobin, Stephanie J.; Osika, Matylda M.; McLanders, Mia

    2014-01-01

    People who are high in causal uncertainty doubt their own ability to understand the causes of social events. In three studies, we examined the effects of target and perceiver causal uncertainty on attitudes toward the target. Target causal uncertainty was manipulated via responses on a causal uncertainty scale in Studies 1 and 2, and with a scenario in Study 3. In Studies 1 and 2, we found that participants liked the low causal uncertainty target more than the high causal uncertainty target. This preference was stronger for low relative to high causal uncertainty participants because high causal uncertainty participants held more uncertain ideals. In Study 3, we examined the value individuals place upon causal understanding (causal importance) as an additional moderator. We found that regardless of their own causal uncertainty level, participants who were high in causal importance liked the low causal uncertainty target more than the high causal uncertainty target. However, when participants were low in causal importance, low causal uncertainty perceivers showed no preference and high causal uncertainty perceivers preferred the high causal uncertainty target. These findings reveal that goal importance and ideals can influence how perceivers respond to causal uncertainty in others. PMID:24504048

  17. Attitudes toward others depend upon self and other causal uncertainty.

    PubMed

    Tobin, Stephanie J; Osika, Matylda M; McLanders, Mia

    2014-01-01

    People who are high in causal uncertainty doubt their own ability to understand the causes of social events. In three studies, we examined the effects of target and perceiver causal uncertainty on attitudes toward the target. Target causal uncertainty was manipulated via responses on a causal uncertainty scale in Studies 1 and 2, and with a scenario in Study 3. In Studies 1 and 2, we found that participants liked the low causal uncertainty target more than the high causal uncertainty target. This preference was stronger for low relative to high causal uncertainty participants because high causal uncertainty participants held more uncertain ideals. In Study 3, we examined the value individuals place upon causal understanding (causal importance) as an additional moderator. We found that regardless of their own causal uncertainty level, participants who were high in causal importance liked the low causal uncertainty target more than the high causal uncertainty target. However, when participants were low in causal importance, low causal uncertainty perceivers showed no preference and high causal uncertainty perceivers preferred the high causal uncertainty target. These findings reveal that goal importance and ideals can influence how perceivers respond to causal uncertainty in others.

  18. Cross-sectional schooling-health associations misrepresented causal schooling effects on adult health and health-related behaviors: evidence from the Chinese Adults Twins Survey.

    PubMed

    Behrman, Jere R; Xiong, Yanyan; Zhang, Junsen

    2015-02-01

    Adult health outcomes and health behaviors are often associated with schooling. However, such associations do not necessarily imply that schooling has causal effects on health with the signs or magnitudes found in the cross-sectional associations. Schooling may be proxying for unobserved factors related to genetics and family background that directly affect both health and schooling. Recently several studies have used within-monozygotic (MZ) twins methods to control for unobserved factors shared by identical twins. Within-MZ estimates for developed countries are generally smaller than suggested by cross-sectional associations, consistent with positive correlations between unobserved factors that determine schooling and those that determine health. This study contributes new estimates of cross-sectional associations and within-MZ causal effects using the Chinese Adults Twins Survey, the first study of its type for developing countries. The cross-sectional estimates suggest that schooling is significantly associated with adult health-related behaviors (smoking, drinking, exercising) but not with own or spouse health outcomes (general health, mental health, overweight, chronic diseases). However, within-MZ-twins estimators change the estimates for approximately half of these health indicators, in one case declining in absolute magnitudes and becoming insignificant and in the other cases increasing in absolute magnitudes. Within-MZ estimates indicate significant pro-health effects for at least one of the indicators for own health (better mental health), own health-related behaviors (less smoking) and spouse health (less overweight). PMID:25464872

  19. Cross-sectional schooling-health associations misrepresented causal schooling effects on adult health and health-related behaviors: Evidence from the Chinese Adults Twins Survey

    PubMed Central

    Behrman, Jere R.; Xiong, Yanyan; Zhang, Junsen

    2015-01-01

    Adult health outcomes and health behaviors are often associated with schooling. However, such associations do not necessarily imply that schooling has causal effects on health with the signs or magnitudes found in the cross-sectional associations. Schooling may be proxying for unobserved factors related to genetics and family background that directly affect both health and schooling. Recently several studies have used within-monozygotic (MZ) twins methods to control for unobserved factors shared by identical twins. Within-MZ estimates for developed countries are generally smaller than suggested by cross-sectional associations, consistent with positive correlations between unobserved factors that determine schooling and those that determine health. This study contributes new estimates of cross-sectional associations and within-MZ causal effects using the Chinese Adults Twins Survey, the first study of its type for developing countries. The cross-sectional estimates suggest that schooling is significantly associated with adult health-related behaviors (smoking, drinking, exercising) but not with own or spouse health outcomes (general health, mental health, overweight, chronic diseases). However, within-MZ-twins estimators change the estimates for approximately half of these health indicators, in one case declining in absolute magnitudes and becoming insignificant and in the other cases increasing in absolute magnitudes. Within-MZ estimates indicate significant pro-health effects for at least one of the indicators for own health (better mental health), own health-related behaviors (less smoking) and spouse health (less overweight). PMID:25464872

  20. 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.

  1. Causal Inference in Public Health

    PubMed Central

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

    2014-01-01

    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. PMID:23297653

  2. Causality and persistence in ecological systems: a nonparametric spectral granger causality approach.

    PubMed

    Detto, Matteo; Molini, Annalisa; Katul, Gabriel; Stoy, Paul; Palmroth, Sari; Baldocchi, Dennis

    2012-04-01

    Abstract Directionality in coupling, defined as the linkage relating causes to their effects at a later time, can be used to explain the core dynamics of ecological systems by untangling direct and feedback relationships between the different components of the systems. Inferring causality from measured ecological variables sampled through time remains a formidable challenge further made difficult by the action of periodic drivers overlapping the natural dynamics of the system. Periodicity in the drivers can often mask the self-sustained oscillations originating from the autonomous dynamics. While linear and direct causal relationships are commonly addressed in the time domain, using the well-established machinery of Granger causality (G-causality), the presence of periodic forcing requires frequency-based statistics (e.g., the Fourier transform), able to distinguish coupling induced by oscillations in external drivers from genuine endogenous interactions. Recent nonparametric spectral extensions of G-causality to the frequency domain pave the way for the scale-by-scale decomposition of causality, which can improve our ability to link oscillatory behaviors of ecological networks to causal mechanisms. The performance of both spectral G-causality and its conditional extension for multivariate systems is explored in quantifying causal interactions within ecological networks. Through two case studies involving synthetic and actual time series, it is demonstrated that conditional G-causality outperforms standard G-causality in identifying causal links and their concomitant timescales.

  3. How prescriptive norms influence causal inferences.

    PubMed

    Samland, Jana; Waldmann, Michael R

    2016-11-01

    Recent experimental findings suggest that prescriptive norms influence causal inferences. The cognitive mechanism underlying this finding is still under debate. We compare three competing theories: The culpable control model of blame argues that reasoners tend to exaggerate the causal influence of norm-violating agents, which should lead to relatively higher causal strength estimates for these agents. By contrast, the counterfactual reasoning account of causal selection assumes that norms do not alter the representation of the causal model, but rather later causal selection stages. According to this view, reasoners tend to preferentially consider counterfactual states of abnormal rather than normal factors, which leads to the choice of the abnormal factor in a causal selection task. A third view, the accountability hypothesis, claims that the effects of prescriptive norms are generated by the ambiguity of the causal test question. Asking whether an agent is a cause can be understood as a request to assess her causal contribution but also her moral accountability. According to this theory norm effects on causal selection are mediated by accountability judgments that are not only sensitive to the abnormality of behavior but also to mitigating factors, such as intentionality and knowledge of norms. Five experiments are presented that favor the accountability account over the two alternative theories. PMID:27591550

  4. Self-esteem and causal attributions.

    PubMed

    Chandler, T A; Lee, M S; Pengilly, J W

    1997-11-01

    The relationship between self-esteem and causal attributions of success and failure in achievement-related behavior was examined among undergraduate students. An integration of a self-consistency model of causal attribution and self-enhancement theory was attempted. Self-esteem and performance outcome conditions of success and failure served as independent variables. Success and failure conditions were created via feedback regarding the participants' performance on an anagram task. The participants' attributions of six causal elements (ability, effort, immediate effort, task difficulty, luck, and mood) were categorized and combined with three causal dimensions (internal-external locus, stability, and controllability), which served as dependent variables. Participants' expectations regarding performance also served as a dependent variable. The relationship between self-esteem, expectancies of success and failure, performance, and stable causality were reported. In terms of causal dimensions, internal, stable, and controllable dimensions were explained by self-enhancement.

  5. Photodetection and causality I

    NASA Astrophysics Data System (ADS)

    de Haan, M.

    1985-09-01

    We investigate in this paper the link between the measurement process in quantum mechanics and casuality associated to a finite velocity of field propagation. We use models of interaction of a bound state with a scalar field. We first show that in the existing approaches, time delays resulting from the finite velocity of propagation have been obtained only as a consequence of ad hoc approximations. We show that exact causality can be restored in the case of a single photodetection by a slight change of the observable associated to the measurement process. Moreover, this modification may be justified by the introduction of a simple model for the photodetector. We present qualitative arguments to show that this procedure cannot be extended to the case of multiple photodetections. The process of repeated photodetection clashes therefore with causality. This paradox is closely related to the Zeno paradox described by Misra and Sudarshan 1). Both may be traced back to the positive definite character of the hamiltonian which is the generator of motion in quantum mechanics.

  6. Spin foam models as energetic causal sets

    NASA Astrophysics Data System (ADS)

    Cortês, Marina; Smolin, Lee

    2016-04-01

    Energetic causal sets are causal sets endowed by a flow of energy-momentum between causally related events. These incorporate a novel mechanism for the emergence of space-time from causal relations [M. Cortês and L. Smolin, Phys. Rev. D 90, 084007 (2014); Phys. Rev. D 90, 044035 (2014)]. Here we construct a spin foam model which is also an energetic causal set model. This model is closely related to the model introduced in parallel by Wolfgang Wieland in [Classical Quantum Gravity 32, 015016 (2015)]. What makes a spin foam model also an energetic causal set is Wieland's identification of new degrees of freedom analogous to momenta, conserved at events (or four-simplices), whose norms are not mass, but the volume of tetrahedra. This realizes the torsion constraints, which are missing in previous spin foam models, and are needed to relate the connection dynamics to those of the metric, as in general relativity. This identification makes it possible to apply the new mechanism for the emergence of space-time to a spin foam model. Our formulation also makes use of Markopoulou's causal formulation of spin foams [arXiv:gr-qc/9704013]. These are generated by evolving spin networks with dual Pachner moves. This endows the spin foam history with causal structure given by a partial ordering of the events which are dual to four-simplices.

  7. 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…

  8. Accelerated relative sea-level rise and rapid coastal erosion: Testing a causal relationship for the Louisiana barrier islands

    USGS Publications Warehouse

    List, J.H.; Sallenger, A.H., Jr.; Hansen, M.E.; Jaffe, B.E.

    1997-01-01

    The role of relative sea-level rise as a cause for the rapid erosion of Louisiana's barrier island coast is investigated through a numerical implementation of a modified Bruun rule that accounts for the low percentage of sand-sized sediment in the eroding Louisiana shoreface. Shore-normal profiles from 150 km of coastline west of the Mississippi delta are derived from bathymetric surveys conducted during the 1880s. 1930s and 1980s. An RMS difference criterion is employed to test whether an equilibrium profile form is maintained between survey years. Only about half the studied profiles meet the equilibrium Criterion this represents a significant limitation on the potential applicability of the Bruun rule. The profiles meeting the equilibrium criterion, along with measured rates of relative sea-level rise, are used to hindcast shoreline retreat rates at 37 locations within the study area. Modeled and observed shoreline retreat rates show no significant correlation. Thus in terms of the Bruun approach relative sea-level rise has no power for hindcasting (and presumably forecasting) rates of coastal erosion for the Louisiana barrier islands.

  9. 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

  10. Granger causality revisited.

    PubMed

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

    2014-11-01

    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.

  11. On the spectral formulation of Granger causality.

    PubMed

    Chicharro, D

    2011-12-01

    Spectral measures of causality are used to explore the role of different rhythms in the causal connectivity between brain regions. We study several spectral measures related to Granger causality, comprising the bivariate and conditional Geweke measures, the directed transfer function, and the partial directed coherence. We derive the formulation of dependence and causality in the spectral domain from the more general formulation in the information-theory framework. We argue that the transfer entropy, the most general measure derived from the concept of Granger causality, lacks a spectral representation in terms of only the processes associated with the recorded signals. For all the spectral measures we show how they are related to mutual information rates when explicitly considering the parametric autoregressive representation of the processes. In this way we express the conditional Geweke spectral measure in terms of a multiple coherence involving innovation variables inherent to the autoregressive representation. We also link partial directed coherence with Sims' criterion of causality. Given our results, we discuss the causal interpretation of the spectral measures related to Granger causality and stress the necessity to explicitly consider their specific formulation based on modeling the signals as linear Gaussian stationary autoregressive processes.

  12. Causal Learning Across Domains

    ERIC Educational Resources Information Center

    Schulz, Laura E.; Gopnik, Alison

    2004-01-01

    Five studies investigated (a) children's ability to use the dependent and independent probabilities of events to make causal inferences and (b) the interaction between such inferences and domain-specific knowledge. In Experiment 1, preschoolers used patterns of dependence and independence to make accurate causal inferences in the domains of…

  13. 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…

  14. 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…

  15. 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

  16. Practical application of the vanishing tetrad test for causal indicator measurement models: an example from health-related quality of life.

    PubMed

    Bollen, Kenneth A; Lennox, Richard D; Dahly, Darren L

    2009-05-01

    Researchers are often faced with the task of trying to measure abstract concepts. The most common approach is to use multiple indicators that reflect an underlying latent variable. However, this 'effect indicator' measurement model is not always appropriate; sometimes the indicators instead cause the construct of interest. While the notion of 'causal indicators' has been known for some time, it is still too often ignored. However, there are limited means to determine whether a possible indicator should be treated as a cause or an effect of the latent construct of interest. Perhaps the best empirical way is to use the vanishing tetrad test (VTT), yet this method is still often overlooked. We speculate that one reason for this is the lack of published examples of its use in practice, written for an audience without extensive statistical training. The goal of this paper was to help fill this gap in the literature-to provide a basic example of how to use the VTT. We illustrated the VTT by looking at multiple items from a health related quality of life instrument that seem more likely to cause the latent variable rather than the other way around.

  17. 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…

  18. 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…

  19. 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…

  20. 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…

  1. Causal conjunction fallacies: the roles of causal strength and mental resources.

    PubMed

    Crisp, Aimée Kay; Feeney, Aidan

    2009-12-01

    In two experiments we tested the prediction derived from Tversky and Kahneman's (1983) work on the causal conjunction fallacy that the strength of the causal connection between constituent events directly affects the magnitude of the causal conjunction fallacy. We also explored whether any effects of perceived causal strength were due to graded output from heuristic Type 1 reasoning processes or the result of analytic Type 2 reasoning processes. As predicted, Experiment 1 demonstrated that fallacy rates were higher for strongly than for weakly related conjunctions. Weakly related conjunctions in turn attracted higher rates of fallacious responding than did unrelated conjunctions. Experiment 2 showed that a concurrent memory load increased rates of fallacious responding for strongly related but not for weakly related conjunctions. We interpret these results as showing that manipulations of the strength of the perceived causal relationship between the conjuncts result in graded output from heuristic reasoning process and that additional mental resources are required to suppress strong heuristic output.

  2. Reading Skill Moderates the Impact of Semantic Similarity and Causal Specificity on the Coherence of Explanations

    ERIC Educational Resources Information Center

    Wittwer, Jörg; Ihme, Natalie

    2014-01-01

    Prior research has shown that readers are sensitive to causal relations between sentences. In addition, the extent to which readers put weight on causal relations seems to depend on their reading skill. Very little attention, however, has been given to the perception of causal relations linguistically expressed by different types of causal verbs…

  3. Causal Rasch models

    PubMed Central

    Stenner, A. Jackson; Fisher, William P.; Stone, Mark H.; Burdick, Donald S.

    2013-01-01

    Rasch's unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured) support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct). We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained. PMID:23986726

  4. The power PC theory and causal powers: comment on Cheng (1997) and Novick and Cheng (2004).

    PubMed

    White, Peter A

    2005-07-01

    It has been claimed that the power PC theory reconciles regularity and power theories of causal judgment by showing how contingency information is used for inferences about unobservable causal powers. Under the causal powers theory causal relations are understood as generative relations in which a causal power of one thing acts on a liability of another thing under some releasing condition. These 3 causal roles are implicit or explicit in all causal interpretations. The power PC theory therefore fails to reconcile power theories and regularity theories because it has a fundamentally different definition of power and does not accommodate the tripartite causal role distinction. Implications of this distinction are drawn out.

  5. Dimensions of Causal Understanding: The Role of Complex Causal Models in Students' Understanding of Science

    ERIC Educational Resources Information Center

    Perkins, David N.; Grotzer, Tina A.

    2005-01-01

    This article argues that an important source of the difficulties posed by particular concepts and theories is the narrow range of "types of causal models" with which most learners are familiar. Most learners are familiar with relatively simple styles of causal models, but many concepts and theories in science depend on styles substantially more…

  6. 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

  7. Causal localizations in relativistic quantum mechanics

    SciTech Connect

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

    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 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.

  8. The gravity dual of boundary causality

    NASA Astrophysics Data System (ADS)

    Engelhardt, Netta; Fischetti, Sebastian

    2016-09-01

    In gauge/gravity duality, points which are not causally related on the boundary cannot be causally related through the bulk; this is the statement of boundary causality. By the Gao-Wald theorem, the averaged null energy condition in the bulk is sufficient to ensure this property. Here we proceed in the converse direction: we derive a necessary as well as sufficient condition for the preservation of boundary causality under perturbative (quantum or stringy) corrections to the bulk. The condition that we find is a (background-dependent) constraint on the amount by which light cones can ‘open’ over all null bulk geodesics. We show that this constraint is weaker than the averaged null energy condition.

  9. The gravity dual of boundary causality

    NASA Astrophysics Data System (ADS)

    Engelhardt, Netta; Fischetti, Sebastian

    2016-09-01

    In gauge/gravity duality, points which are not causally related on the boundary cannot be causally related through the bulk; this is the statement of boundary causality. By the Gao–Wald theorem, the averaged null energy condition in the bulk is sufficient to ensure this property. Here we proceed in the converse direction: we derive a necessary as well as sufficient condition for the preservation of boundary causality under perturbative (quantum or stringy) corrections to the bulk. The condition that we find is a (background-dependent) constraint on the amount by which light cones can ‘open’ over all null bulk geodesics. We show that this constraint is weaker than the averaged null energy condition.

  10. 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

  11. 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.

  12. Information causality from an entropic and a probabilistic perspective

    SciTech Connect

    Al-Safi, Sabri W.; Short, Anthony J.

    2011-10-15

    The information causality principle is a generalization of the no-signaling principle which implies some of the known restrictions on quantum correlations. But despite its clear physical motivation, information causality is formulated in terms of a rather specialized game and figure of merit. We explore different perspectives on information causality, discussing the probability of success as the figure of merit, a relation between information causality and the nonlocal ''inner-product game,'' and the derivation of a quadratic bound for these games. We then examine an entropic formulation of information causality with which one can obtain the same results, arguably in a simpler fashion.

  13. Causal inference with multiple time series: principles and problems.

    PubMed

    Eichler, Michael

    2013-08-28

    I review the use of the concept of Granger causality for causal inference from time-series data. First, I give a theoretical justification by relating the concept to other theoretical causality measures. Second, I outline possible problems with spurious causality and approaches to tackle these problems. Finally, I sketch an identification algorithm that learns causal time-series structures in the presence of latent variables. The description of the algorithm is non-technical and thus accessible to applied scientists who are interested in adopting the method.

  14. Heavier smoking may lead to a relative increase in waist circumference: evidence for a causal relationship from a Mendelian randomisation meta-analysis. The CARTA consortium

    PubMed Central

    Morris, Richard W; Taylor, Amy E; Fluharty, Meg E; Bjørngaard, Johan H; Åsvold, Bjørn Olav; Elvestad Gabrielsen, Maiken; Campbell, Archie; Marioni, Riccardo; Kumari, Meena; Korhonen, Tellervo; Männistö, Satu; Marques-Vidal, Pedro; Kaakinen, Marika; Cavadino, Alana; Postmus, Iris; Husemoen, Lise Lotte N; Skaaby, Tea; Ahluwalia, Tarun Veer Singh; Treur, Jorien L; Willemsen, Gonneke; Dale, Caroline; Wannamethee, S Goya; Lahti, Jari; Palotie, Aarno; Räikkönen, Katri; McConnachie, Alex; Padmanabhan, Sandosh; Wong, Andrew; Dalgård, Christine; Paternoster, Lavinia; Ben-Shlomo, Yoav; Tyrrell, Jessica; Horwood, John; Fergusson, David M; Kennedy, Martin A; Nohr, Ellen A; Christiansen, Lene; Kyvik, Kirsten Ohm; Kuh, Diana; Watt, Graham; Eriksson, Johan G; Whincup, Peter H; Vink, Jacqueline M; Boomsma, Dorret I; Davey Smith, George; Lawlor, Debbie; Linneberg, Allan; Ford, Ian; Jukema, J Wouter; Power, Chris; Hyppönen, Elina; Jarvelin, Marjo-Riitta; Preisig, Martin; Borodulin, Katja; Kaprio, Jaakko; Kivimaki, Mika; Smith, Blair H; Hayward, Caroline; Romundstad, Pål R; Sørensen, Thorkild I A; Munafò, Marcus R; Sattar, Naveed

    2015-01-01

    Objectives To investigate, using a Mendelian randomisation approach, whether heavier smoking is associated with a range of regional adiposity phenotypes, in particular those related to abdominal adiposity. Design Mendelian randomisation meta-analyses using a genetic variant (rs16969968/rs1051730 in the CHRNA5-CHRNA3-CHRNB4 gene region) as a proxy for smoking heaviness, of the associations of smoking heaviness with a range of adiposity phenotypes. Participants 148 731 current, former and never-smokers of European ancestry aged ≥16 years from 29 studies in the consortium for Causal Analysis Research in Tobacco and Alcohol (CARTA). Primary outcome measures Waist and hip circumferences, and waist-hip ratio. Results The data included up to 66 809 never-smokers, 43 009 former smokers and 38 913 current daily cigarette smokers. Among current smokers, for each extra minor allele, the geometric mean was lower for waist circumference by −0.40% (95% CI −0.57% to −0.22%), with effects on hip circumference, waist-hip ratio and body mass index (BMI) being −0.31% (95% CI −0.42% to −0.19), −0.08% (−0.19% to 0.03%) and −0.74% (−0.96% to −0.51%), respectively. In contrast, among never-smokers, these effects were higher by 0.23% (0.09% to 0.36%), 0.17% (0.08% to 0.26%), 0.07% (−0.01% to 0.15%) and 0.35% (0.18% to 0.52%), respectively. When adjusting the three central adiposity measures for BMI, the effects among current smokers changed direction and were higher by 0.14% (0.05% to 0.22%) for waist circumference, 0.02% (−0.05% to 0.08%) for hip circumference and 0.10% (0.02% to 0.19%) for waist-hip ratio, for each extra minor allele. Conclusions For a given BMI, a gene variant associated with increased cigarette consumption was associated with increased waist circumference. Smoking in an effort to control weight may lead to accumulation of central adiposity. PMID:26264275

  15. Sensing the Coherence of Biology in Contrast to Psychology: Young Children’s Use of Causal Relations to Distinguish Two Foundational Domains

    PubMed Central

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

    2011-01-01

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

  16. Considerations on causality in pharmacovigilance.

    PubMed

    Edwards, I Ralph

    2012-01-01

    Causality has been a topic of debate by philosophers, scientists, lawyers and for centuries. It is essential to define as precisely as possible all steps in the logical chain of events, since each may strengthen or confound an argument. Almost always there are issues of missing and conflicting data that need to be addressed specifically. In pharmacovigilance, as in many other situations, there is not just one possible causation for an effect but several. Each must be evaluated in the given context for probability. There is also likely to be a causal chain of events to the adverse effect under consideration, and each must be considered. In an individual patient diagnosis the components of patient history, clinical findings and various laboratory test findings are combined to point to the probability of the patho-physiological diagnosis, which in turn is related to possible causes with a strength determined by the constellation of findings. The established Bradford-Hill criteria are valuable in considering all the possible causal factors. Pharmacoepidemiology allows for population incidences of causes for particular effects and therefore provides an a priori probability listing for competing possible causes, or at least of one possible cause against the background of all others in a control group. Since adverse effects of medicines are generally rare, it is not possible to exclude drug causation in an individual by reliance on epidemiological evidence alone, only to argue that the incidence is below a level determined by statistical power, of the study or studies combined. Other areas of society are concerned with the process of causal inference, and this is especially true in legal cases in which judgements are made on possible personal injury by drugs.

  17. 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.

  18. Rate-Agnostic (Causal) Structure Learning

    PubMed Central

    Plis, Sergey; Danks, David; Freeman, Cynthia; Calhoun, Vince

    2016-01-01

    Causal structure learning from time series data is a major scientific challenge. Extant algorithms assume that measurements occur sufficiently quickly; more precisely, they assume approximately equal system and measurement timescales. In many domains, however, measurements occur at a significantly slower rate than the underlying system changes, but the size of the timescale mismatch is often unknown. This paper develops three causal structure learning algorithms, each of which discovers all dynamic causal graphs that explain the observed measurement data, perhaps given undersampling. That is, these algorithms all learn causal structure in a “rate-agnostic” manner: they do not assume any particular relation between the measurement and system timescales. We apply these algorithms to data from simulations to gain insight into the challenge of undersampling. PMID:27182188

  19. 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

  20. Evaluating Causal Models.

    ERIC Educational Resources Information Center

    Watt, James H., Jr.

    Pointing out that linear causal models can organize the interrelationships of a large number of variables, this paper contends that such models are particularly useful to mass communication research, which must by necessity deal with complex systems of variables. The paper first outlines briefly the philosophical requirements for establishing a…

  1. Causal Responsibility and Counterfactuals

    ERIC Educational Resources Information Center

    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…

  2. Causal Premise Semantics

    ERIC Educational Resources Information Center

    Kaufmann, Stefan

    2013-01-01

    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…

  3. The Causal Asymmetry

    ERIC Educational Resources Information Center

    White, Peter A.

    2006-01-01

    It is hypothesized that there is a pervasive and fundamental bias in humans' understanding of physical causation: Once the roles of cause and effect are assigned to objects in interactions, people tend to overestimate the strength and importance of the causal object and underestimate that of the effect object in bringing about the outcome. This…

  4. Causality: Physics and Philosophy

    ERIC Educational Resources Information Center

    Chatterjee, Atanu

    2013-01-01

    Nature is a complex causal network exhibiting diverse forms and species. These forms or rather systems are physically open, structurally complex and naturally adaptive. They interact with the surrounding media by operating a positive-feedback loop through which, they adapt, organize and self-organize themselves in response to the ever-changing…

  5. Models and Moves: Focusing on Dimensions of Causal Complexity To Achieve Deeper Scientific Understanding.

    ERIC Educational Resources Information Center

    Perkins, David N.; Grotzer, Tina A.

    This paper presents the results of a research project based on the Understandings of Consequence Project. This study motivated students to engage in inquiry in science classrooms. The complexity of the models is divided into four categories--underlying causality, relational causality, probabilistic causality, and emergent causality--and provides…

  6. 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

  7. Hymenoscyphus fraxineus vs. Hymenoscyphus albidus – A comparative light microscopic study on the causal agent of European ash dieback and related foliicolous, stroma-forming species

    PubMed Central

    Baral, Hans-Otto; Bemmann, Martin

    2014-01-01

    Five species of Hymenoscyphus that fruit on black stromatized parts of dead leaves of deciduous trees are presented, giving details on their morphological and ecological characteristics. Several of these species have previously been misplaced in rutstroemiaceous genera because of the presence of a substratal stroma. However, the heteropolar, scutuloid ascospores with an often hook-like lateral protrusion at the rounded apex and the ascus apical ring of the Hymenoscyphus-type represent two reliable morphological characteristics that, together with molecular data, provide clear evidence for their placement in the genus Hymenoscyphus (Helotiaceae). Among the species treated is Hymenoscyphus fraxineus (=Hymenoscyphus pseudoalbidus), the causal agent of the European ash dieback disease. Since 1992 this species started within Europe to replace the rather uncommon Hymenoscyphus albidus, which is likewise confined to leaves of Fraxinus. Hy. fraxineus has been recorded already since 1990 in Eastern Asia (Japan, Korea, northeast of China), where it had been initially misidentified as Lambertella albida (≡Hy. albidus). In these regions, it occurs as a harmless saprotroph on Fraxinus mandshurica and Fraxinus rhynchophylla, suggesting that those populations are native while the European ash dieback disease has a recent Eastern Asiatic origin. The distinctly higher genetic diversity found in Japanese Hy. fraxineus in contrast to European Hy. fraxineus supports this view. Genetic similarities between Japanese Hy. fraxineus and European Hy. albidus suggest that also Hy. albidus might be a descendant of Asian Hy. fraxineus, though having invaded Europe much earlier. However, consistent genetic deviation between European and Asian Hy. fraxineus at two nucleotide positions of the ITS region indicates that the European ash disease originates from a region different from the presently known areas in Eastern Asia. Our results underline the importance of detailed morphological studies

  8. 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.…

  9. Context, causality, and appreciation.

    PubMed

    Ross, Stephanie

    2013-04-01

    I applaud and elaborate on the contextualism at the heart of Bullot & Reber's (B&R's) theory, challenge two aspects of the appreciative structure they posit (the causal reasoning that allegedly underlies the design stance and the segregation of the component stages), suggest that expert and novice appreciators operate differently, and question the degree to which B&R's final theory is open to empirical investigation. PMID:23507111

  10. Context, causality, and appreciation.

    PubMed

    Ross, Stephanie

    2013-04-01

    I applaud and elaborate on the contextualism at the heart of Bullot & Reber's (B&R's) theory, challenge two aspects of the appreciative structure they posit (the causal reasoning that allegedly underlies the design stance and the segregation of the component stages), suggest that expert and novice appreciators operate differently, and question the degree to which B&R's final theory is open to empirical investigation.

  11. 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

  12. Are fetal growth impairment and preterm birth causally related to child attention problems and ADHD? Evidence from a comparison between high-income and middle-income cohorts

    PubMed Central

    Murray, Elizabeth; Pearson, Rebecca; Fernandes, Michelle; Santos, Iná S; Barros, Fernando C; Victora, Cesar G; Stein, Alan; Matijasevich, Alicia

    2016-01-01

    Background Cross-cohort comparison is an established method for improving causal inference. This study compared 2 cohorts, 1 from a high-income country and another from a middle-income country, to (1) establish whether birth exposures may play a causal role in the development of childhood attention problems; and (2) identify whether confounding structures play a different role in parent-reported attention difficulties compared with attention deficit hyperactivity disorder (ADHD) diagnoses. Methods Birth exposures included low birth weight (LBW), small-for-gestational age (SGA), small head circumference (HC) and preterm birth (PTB)). Outcomes of interest were attention difficulties (Strengths and Difficulties Questionnaire, SDQ) and ADHD (Development and Well-Being Assessment, DAWBA). Associations between exposures and outcomes were compared between 7-year-old children from the Avon Longitudinal Study of Parents and Children (ALSPAC) in the UK (N=6849) and the 2004 Pelotas cohort in Brazil (N=3509). Results For attention difficulties (SDQ), the pattern of association with birth exposures was similar between cohorts: following adjustment, attention difficulties were associated with SGA (OR=1.59, 95% CI 1.20 to 2.19) and small HC (OR=1.64, 95% CI 1.11 to 2.41) in ALSPAC and SGA (OR=1.35, 95% CI 1.04 to 1.75) in Pelotas. For ADHD, however, the pattern of association following adjustment differed markedly between cohorts. In ALSPAC, ADHD was associated with LBW (OR=2.29, 95% CI 1.09 to 4.80) and PTB (OR=2.33, 95% CI 1.23 to 4.42). In the Pelotas cohort, however, ADHD was associated with SGA (OR=1.69, 95% CI 1.02 to 2.82). Conclusions The findings suggest that fetal growth impairment may play a causal role in the development of attention difficulties in childhood, as similar associations were identified across both cohorts. Confounding structures, however, appear to play a greater role in determining whether a child meets the full diagnostic criteria for ADHD. PMID

  13. 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…

  14. Linear structures, causal sets and topology

    NASA Astrophysics Data System (ADS)

    Hudetz, Laurenz

    2015-11-01

    Causal set theory and the theory of linear structures (which has recently been developed by Tim Maudlin as an alternative to standard topology) share some of their main motivations. In view of that, I raise and answer the question how these two theories are related to each other and to standard topology. I show that causal set theory can be embedded into Maudlin's more general framework and I characterise what Maudlin's topological concepts boil down to when applied to discrete linear structures that correspond to causal sets. Moreover, I show that all topological aspects of causal sets that can be described in Maudlin's theory can also be described in the framework of standard topology. Finally, I discuss why these results are relevant for evaluating Maudlin's theory. The value of this theory depends crucially on whether it is true that (a) its conceptual framework is as expressive as that of standard topology when it comes to describing well-known continuous as well as discrete models of spacetime and (b) it is even more expressive or fruitful when it comes to analysing topological aspects of discrete structures that are intended as models of spacetime. On one hand, my theorems support (a). The theory is rich enough to incorporate causal set theory and its definitions of topological notions yield a plausible outcome in the case of causal sets. On the other hand, the results undermine (b). Standard topology, too, has the conceptual resources to capture those topological aspects of causal sets that are analysable within Maudlin's framework. This fact poses a challenge for the proponents of Maudlin's theory to prove it fruitful.

  15. Causal Entropic Forces

    NASA Astrophysics Data System (ADS)

    Wissner-Gross, A. D.; Freer, C. E.

    2013-04-01

    Recent advances in fields ranging from cosmology to computer science have hinted at a possible deep connection between intelligence and entropy maximization, but no formal physical relationship between them has yet been established. Here, we explicitly propose a first step toward such a relationship in the form of a causal generalization of entropic forces that we find can cause two defining behaviors of the human “cognitive niche”—tool use and social cooperation—to spontaneously emerge in simple physical systems. Our results suggest a potentially general thermodynamic model of adaptive behavior as a nonequilibrium process in open systems.

  16. Quantum information causality.

    PubMed

    Pitalúa-García, Damián

    2013-05-24

    How much information can a transmitted physical system fundamentally communicate? We introduce the principle of quantum information causality, which states the maximum amount of quantum information that a quantum system can communicate as a function of its dimension, independently of any previously shared quantum physical resources. We present a new quantum information task, whose success probability is upper bounded by the new principle, and show that an optimal strategy to perform it combines the quantum teleportation and superdense coding protocols with a task that has classical inputs. PMID:23745844

  17. Fast causal multicast

    NASA Technical Reports Server (NTRS)

    Birman, Kenneth P.; Schiper, Andre; Stephenson, Pat

    1990-01-01

    A new protocol is presented that efficiently implements a reliable, causally ordered multicast primitive and is easily extended into a totally ordered one. Intended for use in the ISIS toolkit, it offers a way to bypass the most costly aspects of ISIS while benefiting from virtual synchrony. The facility scales with bounded overhead. Measured speedups of more than an order of magnitude were obtained when the protocol was implemented within ISIS. One conclusion is that systems such as ISIS can achieve performance competitive with the best existing multicast facilities - a finding contradicting the widespread concern that fault-tolerance may be unacceptably costly.

  18. Granger causality and transfer entropy are equivalent for Gaussian variables.

    PubMed

    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.

  19. On causality in polymer scalar field theory

    NASA Astrophysics Data System (ADS)

    García-Chung, Angel A.; Morales-Técotl, Hugo A.

    2011-10-01

    The properties of spacetime corresponding to a proposed quantum gravity theory might modify the high energy behavior of quantum fields. Motivated by loop quantum gravity, recently, Hossain et al [1] have considered a polymer field algebra that replaces the standard canonical one in order to calculate the propagator of a real scalar field in flat spacetime. This propagator features Lorentz violations. Motivated by the relation between Lorentz invariance and causality in standard Quantum Field Theory, in this work we investigate the causality behavior of the polymer scalar field.

  20. Causal Phenotype Discovery via Deep Networks

    PubMed Central

    Kale, David C.; Che, Zhengping; Bahadori, Mohammad Taha; Li, Wenzhe; Liu, Yan; Wetzel, Randall

    2015-01-01

    The rapid growth of digital health databases has attracted many researchers interested in using modern computational methods to discover and model patterns of health and illness in a research program known as computational phenotyping. Much of the work in this area has focused on traditional statistical learning paradigms, such as classification, prediction, clustering, pattern mining. In this paper, we propose a related but different paradigm called causal phenotype discovery, which aims to discover latent representations of illness that are causally predictive. We illustrate this idea with a two-stage framework that combines the latent representation learning power of deep neural networks with state-of-the-art tools from causal inference. We apply this framework to two large ICU time series data sets and show that it can learn features that are predictively useful, that capture complex physiologic patterns associated with critical illnesses, and that are potentially more clinically meaningful than manually designed features. PMID:26958203

  1. Causal binding of actions to their effects.

    PubMed

    Buehner, Marc J; Humphreys, Gruffydd R

    2009-10-01

    According to widely held views in cognitive science harking back to David Hume, causality cannot be perceived directly, but instead is inferred from patterns of sensory experience, and the quality of these inferences is determined by perceivable quantities such as contingency and contiguity. We report results that suggest a reversal of Hume's conjecture: People's sense of time is warped by the experience of causality. In a stimulus-anticipation task, participants' response behavior reflected a shortened experience of time in the case of target stimuli participants themselves had generated, relative to equidistant, equally predictable stimuli they had not caused. These findings suggest that causality in the mind leads to temporal binding of cause and effect, and extend and generalize beyond earlier claims of intentional binding between action and outcome.

  2. 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

  3. 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.

  4. 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. PMID:27532045

  5. 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.

  6. Redundant variables and Granger causality.

    PubMed

    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.

  7. 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…

  8. The Role of Functional Form in Causal-Based Categorization

    ERIC Educational Resources Information Center

    Rehder, Bob

    2015-01-01

    Two experiments tested how the "functional form" of the causal relations that link features of categories affects category-based inferences. Whereas "independent causes" can each bring about an effect by themselves, "conjunctive causes" all need to be present for an effect to occur. The causal model view of category…

  9. Causal Discourse Analyzer: Improving Automated Feedback on Academic ESL Writing

    ERIC Educational Resources Information Center

    Chukharev-Hudilainen, Evgeny; Saricaoglu, Aysel

    2016-01-01

    Expressing causal relations plays a central role in academic writing. While it is important that writing instructors assess and provide feedback on learners' causal discourse, it could be a very time-consuming task. In this respect, automated writing evaluation (AWE) tools may be helpful. However, to date, there have been no AWE tools capable of…

  10. 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…

  11. 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…

  12. 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…

  13. Causality violation and paradoxes

    NASA Astrophysics Data System (ADS)

    Krasnikov, S. V.

    1997-03-01

    Paradoxes that can supposedly occur if causality is violated are discussed. It is shown that the existence of ``trajectories of multiplicity zero'' (i.e., trajectories that describe, say, a ball hitting its younger self so that the latter cannot fall into the time machine) is not paradoxical by itself. This apparent paradox can be resolved (at least sometimes) without any harm to local physics or to the time machine. Also a simple model is adduced for which the absence of true paradoxes caused by self-interaction in an acausal world is proved. The conclusion is made that the paradoxes appear if and (within this model) only if the fact is neglected that no conditions fixed to the past of a time machine guarantee that a system remains isolated after it intersects the Cauchy horizon.

  14. Causal electromagnetic interaction equations

    SciTech Connect

    Zinoviev, Yury M.

    2011-02-15

    For the electromagnetic interaction of two particles the relativistic causal quantum mechanics equations are proposed. These equations are solved for the case when the second particle moves freely. The initial wave functions are supposed to be smooth and rapidly decreasing at the infinity. This condition is important for the convergence of the integrals similar to the integrals of quantum electrodynamics. We also consider the singular initial wave functions in the particular case when the second particle mass is equal to zero. The discrete energy spectrum of the first particle wave function is defined by the initial wave function of the free-moving second particle. Choosing the initial wave functions of the free-moving second particle it is possible to obtain a practically arbitrary discrete energy spectrum.

  15. Visual Causality Judgments Correlate with the Phase of Alpha Oscillations.

    PubMed

    Cravo, Andre Mascioli; Santos, Karin Moreira; Reyes, Marcelo Bussotti; Caetano, Marcelo Salvador; Claessens, Peter M E

    2015-10-01

    The detection of causality is essential for our understanding of whether distinct events relate. A central requirement for the sensation of causality is temporal contiguity: As the interval between events increases, causality ratings decrease; for intervals longer than approximately 100 msec, the events start to appear independent. It has been suggested that this effect might be due to perception relying on discrete processing. According to this view, two events may be judged as sequential or simultaneous depending on their temporal relationship within a discrete neuronal process. To assess if alpha oscillations underlie this discrete neuronal process, we investigated how these oscillations modulate the judgment of causality. We used the classic launching effect with concurrent recording of EEG signal. In each trial, a disk moved horizontally toward a second disk at the center of the screen and stopped when they touched each other. After a delay that varied between 0 and 400 msec after contact, the right disk began to move. Participants were instructed to judge whether or not they had a feeling that the first disk caused the movement of the second disk. We found that frontocentral alpha phase significantly biased causality estimates. Moreover, we found that alpha phase was concentrated around different angles for trials in which participants judged events as causally related versus not causally related. We conclude that alpha phase plays a key role in biasing causality judgments.

  16. The lesson of causal discovery algorithms for quantum correlations: causal explanations of Bell-inequality violations require fine-tuning

    NASA Astrophysics Data System (ADS)

    Wood, Christopher J.; Spekkens, Robert W.

    2015-03-01

    An active area of research in the fields of machine learning and statistics is the development of causal discovery algorithms, the purpose of which is to infer the causal relations that hold among a set of variables from the correlations that these exhibit . We apply some of these algorithms to the correlations that arise for entangled quantum systems. We show that they cannot distinguish correlations that satisfy Bell inequalities from correlations that violate Bell inequalities, and consequently that they cannot do justice to the challenges of explaining certain quantum correlations causally. Nonetheless, by adapting the conceptual tools of causal inference, we can show that any attempt to provide a causal explanation of nonsignalling correlations that violate a Bell inequality must contradict a core principle of these algorithms, namely, that an observed statistical independence between variables should not be explained by fine-tuning of the causal parameters. In particular, we demonstrate the need for such fine-tuning for most of the causal mechanisms that have been proposed to underlie Bell correlations, including superluminal causal influences, superdeterminism (that is, a denial of freedom of choice of settings), and retrocausal influences which do not introduce causal cycles.

  17. Bell's theorem and the causal arrow of time

    NASA Astrophysics Data System (ADS)

    Argaman, Nathan

    2010-10-01

    Einstein held that the formalism of quantum mechanics involves "spooky actions at a distance." In the 1960s, Bell amplified this by showing that the predictions of quantum mechanics disagree with the results of any locally causal description. It should be appreciated that accepting nonlocal descriptions while retaining causality leads to a clash with relativity. Furthermore, the causal arrow of time by definition contradicts time-reversal symmetry. For these reasons, Wheeler and Feynman, Costa de Beauregard, Cramer, Price, and others have advocated abandoning microscopic causality. In this paper, a simplistic but concrete example of this line of thought is presented, in the form of a retro-causal toy model that is stochastic and provides an appealing description of the quantum correlations discussed by Bell. It is concluded that Einstein's "spooky actions" may occur "in the past" rather than "at a distance," resolving the tension between quantum mechanics and relativity and opening unexplored possibilities for future reformulations of quantum mechanics.

  18. Mitigating the effects of measurement noise on Granger causality

    SciTech Connect

    Nalatore, Hariharan; Ding Mingzhou; Rangarajan, Govindan

    2007-03-15

    Computing Granger causal relations among bivariate experimentally observed time series has received increasing attention over the past few years. Such causal relations, if correctly estimated, can yield significant insights into the dynamical organization of the system being investigated. Since experimental measurements are inevitably contaminated by noise, it is thus important to understand the effects of such noise on Granger causality estimation. The first goal of this paper is to provide an analytical and numerical analysis of this problem. Specifically, we show that, due to noise contamination (1) spurious causality between two measured variables can arise and (2) true causality can be suppressed. The second goal of the paper is to provide a denoising strategy to mitigate this problem. Specifically, we propose a denoising algorithm based on the combined use of the Kalman filter theory and the expectation-maximization algorithm. Numerical examples are used to demonstrate the effectiveness of the denoising approach.

  19. Causal Poisson bracket via deformation quantization

    NASA Astrophysics Data System (ADS)

    Berra-Montiel, Jasel; Molgado, Alberto; Palacios-García, César D.

    2016-06-01

    Starting with the well-defined product of quantum fields at two spacetime points, we explore an associated Poisson structure for classical field theories within the deformation quantization formalism. We realize that the induced star-product is naturally related to the standard Moyal product through an appropriate causal Green’s functions connecting points in the space of classical solutions to the equations of motion. Our results resemble the Peierls-DeWitt bracket that has been analyzed in the multisymplectic context. Once our star-product is defined, we are able to apply the Wigner-Weyl map in order to introduce a generalized version of Wick’s theorem. Finally, we include some examples to explicitly test our method: the real scalar field, the bosonic string and a physically motivated nonlinear particle model. For the field theoretic models, we have encountered causal generalizations of the creation/annihilation relations, and also a causal generalization of the Virasoro algebra for the bosonic string. For the nonlinear particle case, we use the approximate solution in terms of the Green’s function, in order to construct a well-behaved causal bracket.

  20. 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…

  1. 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…

  2. 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…

  3. 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…

  4. [Causality: risk factors and interventions].

    PubMed

    Dekkers, Olaf M; Vandenbroucke, Jan P

    2013-01-01

    A risk factor has a causal effect on a disease when the disease would not have occurred in the absence of the risk factor. Analogous reasoning applies to the effect of a particular therapy. Thinking in terms of contrasts is fundamental to causal reasoning in medicine. The contrast determines the content of the causal claim; the most important assumption here is that the prognosis between groups is comparable. Causal effects of risk factors are not always the same as the causal effect of an intervention: removal of a risk factor (e.g. smoking) for a disease does not necessarily mean that the risk will subsequently normalize. A second problem is that risk factors cannot always easily be translated into interventions. This applies to factors that cannot be changed (e.g. gender) or that can have multiple causes themselves (e.g. obesity).

  5. CausalTrail: Testing hypothesis using causal Bayesian networks

    PubMed Central

    Trampert, Patrick; Lenhof, Hans-Peter

    2015-01-01

    Summary Causal Bayesian Networks are a special class of Bayesian networks in which the hierarchy directly encodes the causal relationships between the variables. This allows to compute the effect of interventions, which are external changes to the system, caused by e.g. gene knockouts or an administered drug. Whereas numerous packages for constructing causal Bayesian networks are available, hardly any program targeted at downstream analysis exists. In this paper we present CausalTrail, a tool for performing reasoning on causal Bayesian networks using the do-calculus. CausalTrail's features include multiple data import methods, a flexible query language for formulating hypotheses, as well as an intuitive graphical user interface. The program is able to account for missing data and thus can be readily applied in multi-omics settings where it is common that not all measurements are performed for all samples. Availability and Implementation CausalTrail is implemented in C++ using the Boost and Qt5 libraries. It can be obtained from https://github.com/dstoeckel/causaltrail PMID:26913195

  6. 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

  7. Causal beliefs about depression in different cultural groups-what do cognitive psychological theories of causal learning and reasoning predict?

    PubMed

    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.

  8. Causal beliefs about depression in different cultural groups-what do cognitive psychological theories of causal learning and reasoning predict?

    PubMed

    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

  9. History, causality, and sexology.

    PubMed

    Money, John

    2003-08-01

    In 1896, Krafft-Ebing published Psychopathia Sexualis. Popularly defined as hereditary weakness or taintedness in the family pedigree, degeneracy was called upon as a causal explanation for perversions of the sexual instinct. Although Krafft-Ebing accepted Karl Ulrichs proposal that homosexuality could be innate and probably located in the brain, he paid little attention to neuropathological sexology. Alfred Binet challenged Krafft-Ebing's orthodoxy by explaining fetishism in terms of associative learning, to which Krafft-Ebing's response was that only those with a hereditary taint would be vulnerable. Thus did the venerable nature-nurture antithesis maintain its rhetoric, even to the present day. Krafft-Ebing died too soon to meet the Freudian challenge of endopsychic determinism, and too soon also to encounter the idea of a developmental multivariate outcome of what I have termed the lovemap. Like other brain maps, for example the languagemap, the lovemap requires an intact human brain in which to develop. The personalized content of the lovemap has access to the brain by way of the special senses.

  10. 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

  11. Income inequality and health: a causal review.

    PubMed

    Pickett, Kate E; Wilkinson, Richard G

    2015-03-01

    There is a very large literature examining income inequality in relation to health. Early reviews came to different interpretations of the evidence, though a large majority of studies reported that health tended to be worse in more unequal societies. More recent studies, not included in those reviews, provide substantial new evidence. Our purpose in this paper is to assess whether or not wider income differences play a causal role leading to worse health. We conducted a literature review within an epidemiological causal framework and inferred the likelihood of a causal relationship between income inequality and health (including violence) by considering the evidence as a whole. The body of evidence strongly suggests that income inequality affects population health and wellbeing. The major causal criteria of temporality, biological plausibility, consistency and lack of alternative explanations are well supported. Of the small minority of studies which find no association, most can be explained by income inequality being measured at an inappropriate scale, the inclusion of mediating variables as controls, the use of subjective rather than objective measures of health, or follow up periods which are too short. The evidence that large income differences have damaging health and social consequences is strong and in most countries inequality is increasing. Narrowing the gap will improve the health and wellbeing of populations.

  12. [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.

  13. Space, Time, and Causality in the Human Brain

    PubMed Central

    Woods, Adam J.; Hamilton, Roy H.; Kranjec, Alexander; Minhaus, Preet; Bikson, Marom; Yu, Jonathan; Chatterjee, Anjan

    2014-01-01

    The ability to perceive causality is a central human ability constructed from elemental spatial and temporal information present in the environment. Although the nature of causality has captivated philosophers and scientists since antiquity, the neural correlates of causality remain poorly understood. In the present study, we used functional magnetic resonance imaging (fMRI) to generate hypotheses for candidate brain regions related to component processes important for perceptual causality in the human brain: elemental space perception, elemental time perception, and decision-making (Experiment 1; n=16). We then used transcranial direct current stimulation (tDCS) to test neural hypotheses generated from the fMRI experiment (Experiment 2; n=16). In both experiments, participants judged causality in billiard-ball style launching events; a blue ball approaches and contacts a red ball. Spatial and temporal contributions to causal perception were assessed by parametrically varying the spatial linearity and the temporal delays of the movement of the balls. Experiment 1 demonstrated unique patterns of activation correlated with spatial, temporal, and decision-making components of causality perception. Using tDCS, we then tested hypotheses for the specific roles of the parietal and frontal cortices found in the fMRI experiment. Parietal stimulation only decreased participants’ perception of causality based on spatial violations, while frontal stimulation made participants less likely to perceive causality based on violations of space and time. Converging results from fMRI and tDCS indicate that parietal cortices contribute to causal perception because of their specific role in processing spatial relations, while the frontal cortices contribute more generally, consistent with their role in decision-making. PMID:24561228

  14. Space, time, and causality in the human brain.

    PubMed

    Woods, Adam J; Hamilton, Roy H; Kranjec, Alexander; Minhaus, Preet; Bikson, Marom; Yu, Jonathan; Chatterjee, Anjan

    2014-05-15

    The ability to perceive causality is a central human ability constructed from elemental spatial and temporal information present in the environment. Although the nature of causality has captivated philosophers and scientists since antiquity, the neural correlates of causality remain poorly understood. In the present study, we used functional magnetic resonance imaging (fMRI) to generate hypotheses for candidate brain regions related to component processes important for perceptual causality in the human brain: elemental space perception, elemental time perception, and decision-making (Experiment 1; n=16). We then used transcranial direct current stimulation (tDCS) to test neural hypotheses generated from the fMRI experiment (Experiment 2; n=16). In both experiments, participants judged causality in billiard-ball style launching events; a blue ball approaches and contacts a red ball. Spatial and temporal contributions to causal perception were assessed by parametrically varying the spatial linearity and the temporal delays of the movement of the balls. Experiment 1 demonstrated unique patterns of activation correlated with spatial, temporal, and decision-making components of causality perception. Using tDCS, we then tested hypotheses for the specific roles of the parietal and frontal cortices found in the fMRI experiment. Parietal stimulation only decreased participants' perception of causality based on spatial violations, while frontal stimulation made participants less likely to perceive causality based on violations of space and time. Converging results from fMRI and tDCS indicate that parietal cortices contribute to causal perception because of their specific role in processing spatial relations, while the frontal cortices contribute more generally, consistent with their role in decision-making.

  15. 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…

  16. 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. PMID:20801434

  17. 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.

  18. Sentencing goals, causal attributions, ideology, and personality.

    PubMed

    Carroll, J S; Perkowitz, W T; Lurigio, A J; Weaver, F M

    1987-01-01

    Disparity in sentencing of criminals has been related to a variety of individual difference variables. We propose a framework establishing resonances or coherent patterns among sentencing goals, causal attributions, ideology, and personality. Two studies are described, one with law and criminology students, the other with probation officers. Relations among the different types of variables reveal two resonances among both students and officers. One comprises various conservative and moralistic elements: a tough, punitive stance toward crime; belief in individual causality for crime; high scores on authoritarianism, dogmatism, and internal locus of control; lower moral stage; and political conservatism. The second comprises various liberal elements: rehabilitation, belief in economic and other external determinants of crime, higher moral stage, and belief in the powers and responsibilities of government to correct social problems. Implications of these results are discussed for individual differences in sentencing, attribution theory, and attempts to reduce disparity.

  19. 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. Evidence clearly suggests that the relation between reading skills, phoneme awareness, rhyme awareness, and verbal short-term memory is more than a mere association. A strong argument has…

  20. The Significance of Causally Coupled, Stable Neuronal Assemblies for the Psychological Time Arrow

    NASA Astrophysics Data System (ADS)

    Atmanspacher, Harald; Filk, Thomas; Scheingraber, Herbert

    2005-10-01

    Stable neuronal assemblies are generally regarded as neural correlates of mental representations. Their temporal sequence corresponds to the experience of a direction of time, sometimes called the psychological time arrow. We show that the stability of particular, biophysically motivated models of neuronal assemblies, called coupled map lattices, is supported by causal interactions among neurons and obstructed by non-causal or anti-causal interactions among neurons. This surprising relation between causality and stability suggests that those neuronal assemblies that are stable due to causal neuronal interactions, and thus correlated with mental representations, generate a psychological time arrow. Yet this impact of causal interactions among neurons on the directed sequence of mental representations does not rule out the possibility of mentally less efficacious non-causal or anti-causal interactions among neurons.

  1. A Complex Systems Approach to Causal Discovery in Psychiatry

    PubMed Central

    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. PMID:27028297

  2. Body-related state shame and guilt in women: do causal attributions mediate the influence of physical self-concept and shame and guilt proneness.

    PubMed

    Crocker, Peter R E; Brune, Sara M; Kowalski, Kent C; Mack, Diane E; Wilson, Philip M; Sabiston, Catherine M

    2014-01-01

    Guided by the process model of self-conscious emotions, this study examined whether physical self-concept (PSC) and shame and guilt proneness were associated with body-related self-conscious emotions of state shame and guilt and if these relationships were mediated by attributions of stability, globality, and controllability. Female participants (N=284; Mean age=20.6±1.9 years) completed measures of PSC and shame and guilt proneness before reading a hypothetical scenario. Participants completed measures of attributions and state shame and guilt in response to the scenario. Significant relationships were noted between state shame and attributions of globality and controllability, and shame proneness, guilt proneness, and PSC. Similar relationships, with the additional predictor of stability, were found for state guilt. Mediation analysis partially supported the process model hypotheses for shame. Results indicate PSC and shame proneness are important in predicting body-related emotions, but the role of specific attributions are still unclear.

  3. The cosmic microwave background in a causal set universe

    SciTech Connect

    Zuntz, Joe

    2008-02-15

    We discuss cosmic microwave background constraints on the causal set theory of quantum gravity, which has made testable predictions about the nature of dark energy. We flesh out previously discussed heuristic constraints by showing how the power spectrum of causal set dark energy fluctuations can be found from the overlap volumes of past light cones of points in the universe. Using a modified Boltzmann code we put constraints on the single parameter of the theory that are somewhat stronger than previous ones. We conclude that causal set theory cannot explain late-time acceleration without radical alterations to general relativity.

  4. [Causality and prediction: differences and points of contact].

    PubMed

    Silva Ayçaguer, Luis Carlos

    2014-09-10

    This contribution presents the differences between those variables that might play a causal role in a certain process and those only valuable for predicting the outcome. Some considerations are made about the core intervention of the association and the temporal precedence and biases in both cases, the study of causality and predictive modeling. In that context, several relevant aspects related to the design of the corresponding studies are briefly reviewed and some of the mistakes that are often committed in handling both, causality and prediction, are illustrated.

  5. 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

  6. Deconstructing events: the neural bases for space, time, and causality.

    PubMed

    Kranjec, Alexander; Cardillo, Eileen R; Schmidt, Gwenda L; Lehet, Matthew; Chatterjee, Anjan

    2012-01-01

    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 requires one to represent the spatial relations among objects, the relative durations of actions or movements, and the 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 1-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.

  7. Causal Model of a Health Services System

    PubMed Central

    Anderson, James G.

    1972-01-01

    Path analysis is used to construct a causal model of the health services system serving the state of New Mexico. The model includes a network specifying the causal relationships among a set of social, demographic, and economic variables hypothesized to be related to the health status of the population; a set of mathematical equations that permit prediction of the effects of changes in the values of any one variable on all other variables in the model; and estimates of path coefficients based on U.S. Census data and vital statistics. The model is used to predict both direct and indirect effects on health status of changes in population structure resulting from natural causes or from the intervention of health programs. PMID:5025955

  8. Causality in physiological signals.

    PubMed

    Müller, Andreas; Kraemer, Jan F; Penzel, Thomas; Bonnemeier, Hendrik; Kurths, Jürgen; Wessel, Niels

    2016-05-01

    Health is one of the most important non-material assets and thus also has an enormous influence on material values, since treating and preventing diseases is expensive. The number one cause of death worldwide today originates in cardiovascular diseases. For these reasons the aim of understanding the functions and the interactions of the cardiovascular system is and has been a major research topic throughout various disciplines for more than a hundred years. The purpose of most of today's research is to get as much information as possible with the lowest possible effort and the least discomfort for the subject or patient, e.g. via non-invasive measurements. A family of tools whose importance has been growing during the last years is known under the headline of coupling measures. The rationale for this kind of analysis is to identify the structure of interactions in a system of multiple components. Important information lies for example in the coupling direction, the coupling strength, and occurring time lags. In this work, we will, after a brief general introduction covering the development of cardiovascular time series analysis, introduce, explain and review some of the most important coupling measures and classify them according to their origin and capabilities in the light of physiological analyses. We will begin with classical correlation measures, go via Granger-causality-based tools, entropy-based techniques (e.g. momentary information transfer), nonlinear prediction measures (e.g. mutual prediction) to symbolic dynamics (e.g. symbolic coupling traces). All these methods have contributed important insights into physiological interactions like cardiorespiratory coupling, neuro-cardio-coupling and many more. Furthermore, we will cover tools to detect and analyze synchronization and coordination (e.g. synchrogram and coordigram). As a last point we will address time dependent couplings as identified using a recent approach employing ensembles of time series. The

  9. 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…

  10. Introduction a potato cultivar "sprit" as relatively resistant to main fungal pathogens causal agents of early blight and wilting on potato in Iran.

    PubMed

    Saremi, H; Davoodvandy, M H; Amarlou, A

    2007-01-01

    Potato (Solanum tubersum L.) is one of the most human food production cultured in Iran especially Zanjan province as a temperate region. Some fungal pathogens caused severely infected on potato tubers or foliage in the majority grown areas and resulted yield losses in potato production. Recent years from 2002 to 2004 infected samples were collected from different potato grown regions in Zanjan province then cultured on PDA after surface sterilization with sodium hypochlorite. Isolated fungal pathogens were identified and study showed the main pathogens with high incidence and frequency were Alternaria solani, Fusarium oxysporum and Verticillium sp. in studied areas. The regions which used convention varieties showed more diseases than other locations which used relatively resistant races. The rate of resistance for 10 international potato varieties was studied by inoculation of them by 10(5) spores suspension of three common fungal pathogens in the field. Study showed Sprit cultivar was more resistant than others to all three common pathogens and Lady-Claire was most susceptible. Yield production of Sprit per unit of land area was also exceeded that of other cultivars by factors of 1.10 to 2.25 respectively. The results of the study helped potato growers to culture Sprit cultivar and have good yield production in Zanjan and Hamedan provinces in this year.

  11. Are neighborhoods causal? Complications arising from the 'stickiness' of ZNA.

    PubMed

    Glass, Thomas A; Bilal, Usama

    2016-10-01

    Are neighborhoods causal? The answer remains elusive. Armed with new multilevel methods, enthusiasm for neighborhoods research surged at the turn of the century. However, a wave of skepticism has arisen based on the difficulty of drawing causal inferences from observational studies in which selection to neighborhoods is non-random. Researchers have sought answers from experimental and quasi-experimental studies of movers vs. stayers. We develop two related concepts in this essay in the hopes of shedding light on this problem. First, the inceptive environment into which persons are born (which we term ZNA for Zip code Nativity Area) exerts a potentially powerful causal impact on health. Detecting that causal effect is challenging for reasons similar that obtain in other fields (including genetics). Second, we explicate the problem of neighborhood 'stickiness' in terms of the persistence of neighborhood treatment assignment, and argue that under-appreciation of stickiness has led to systematic bias in causal estimates of neighborhoods proportional to the degree of stickiness. In sticky contexts, failure to account for the lasting influences of ZNA by adjusting for intermediate individual socioeconomic and health variables on the causal pathway can result in neighborhood effects estimates that are biased toward the null. We follow with an example drawn from evidence of neighborhood 'stickiness' and obesity. The stickiness of ZNA cautions us that experimental evidence may be insufficient or misleading as a solution to causal inference problems in neighborhood research. PMID:26830654

  12. 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

  13. Spatio-temporal Granger causality: a new framework.

    PubMed

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

    2013-10-01

    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.

  14. Abolishing the effect of reinforcement delay on human causal learning.

    PubMed

    Buehner, Marc J; May, Jon

    2004-04-01

    Associative learning theory postulates two main determinants for human causal learning: contingency and contiguity. In line with such an account, participants in Shanks, Pearson, and Dickinson (1989) failed to discover causal relations involving delays of more than two seconds. More recent research has shown that the impact of contiguity and delay is mediated by prior knowledge about the timeframe of the causal relation in question. Buehner and May (2002, 2003) demonstrated that the detrimental effect of delay can be significantly reduced if reasoners are aware of potential delays. Here we demonstrate for the first time that the negative influence of delay can be abolished completely by a subtle change in the experimental instructions. Temporal contiguity is thus not essential for human causal learning.

  15. An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation.

    PubMed

    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.

  16. 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

  17. 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

  18. 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'). PMID:27111146

  19. Fluctuations in relativistic causal hydrodynamics

    NASA Astrophysics Data System (ADS)

    Kumar, Avdhesh; Bhatt, Jitesh R.; Mishra, Ananta P.

    2014-05-01

    Formalism to calculate the hydrodynamic fluctuations by applying the Onsager theory to the relativistic Navier-Stokes equation is already known. In this work, we calculate hydrodynamic fluctuations within the framework of the second order hydrodynamics of Müller, Israel and Stewart and its generalization to the third order. We have also calculated the fluctuations for several other causal hydrodynamical equations. We show that the form for the Onsager-coefficients and form of the correlation functions remain the same as those obtained by the relativistic Navier-Stokes equation and do not depend on any specific model of hydrodynamics. Further we numerically investigate evolution of the correlation function using the one dimensional boost-invariant (Bjorken) flow. We compare the correlation functions obtained using the causal hydrodynamics with the correlation function for the relativistic Navier-Stokes equation. We find that the qualitative behavior of the correlation functions remains the same for all the models of the causal hydrodynamics.

  20. [Causality in cardiology: concepts in evolution].

    PubMed

    Méndez, Gustavo F

    2005-01-01

    This paper describes several concepts about causality from Empedocles, Aristoteles and Galeno, to Koch and Hill and the evolution of these concepts related to cardiovascular diseases. Also defines cause and risk, and the philosophical theories about scientific knowledge: inductive versus refutation analysis. On these basis, the study of cardiovascular disease's causality, especially coronary heart disease, allows us the identification of several risk factors involved in its development. However, even with the presently coronary heart disease risk charts (from Framingham and European studies) the higher probability for the development of a cardiovascular ischemic event is around 40%, establishing an important degree of uncertainty. With the improvement in molecular biology techniques, genetics have attempted to analyse several genetic polymorphisms in search of the origin of coronary heart disease. Unfortunately, less than 10% of these polymorphisms have had a positive correlation with coronary heart disease being of minor risk that those obtained for having the diagnosis of type 2 diabetes mellitus or hypercholesterolemia. On these basis, the requirement of new population research projects in which clinical and genetic risk factors are to be studied for the appropriate understanding of the causality process of cardiovascular diseases must be a worldwide priority.

  1. 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

  2. Methods for causal inference from gene perturbation experiments and validation

    PubMed Central

    Meinshausen, Nicolai; Hauser, Alain; Mooij, Joris M.; Peters, Jonas; Versteeg, Philip; Bühlmann, Peter

    2016-01-01

    Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodology based on an invariance principle, called invariant causal prediction (ICP). The ICP method quantifies confidence probabilities for inferring causal structures and thus leads to more reliable and confirmatory statements for causal relations and predictions of external intervention effects. We validate the ICP method and some other procedures using large-scale genome-wide gene perturbation experiments in Saccharomyces cerevisiae. The results suggest that prediction and prioritization of future experimental interventions, such as gene deletions, can be improved by using our statistical inference techniques. PMID:27382150

  3. Methods for causal inference from gene perturbation experiments and validation.

    PubMed

    Meinshausen, Nicolai; Hauser, Alain; Mooij, Joris M; Peters, Jonas; Versteeg, Philip; Bühlmann, Peter

    2016-07-01

    Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodology based on an invariance principle, called invariant causal prediction (ICP). The ICP method quantifies confidence probabilities for inferring causal structures and thus leads to more reliable and confirmatory statements for causal relations and predictions of external intervention effects. We validate the ICP method and some other procedures using large-scale genome-wide gene perturbation experiments in Saccharomyces cerevisiae The results suggest that prediction and prioritization of future experimental interventions, such as gene deletions, can be improved by using our statistical inference techniques. PMID:27382150

  4. "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.

  5. Causal interpretation rules for encoding and decoding models in neuroimaging.

    PubMed

    Weichwald, Sebastian; Meyer, Timm; Özdenizci, Ozan; Schölkopf, Bernhard; Ball, Tonio; Grosse-Wentrup, Moritz

    2015-04-15

    Causal terminology is often introduced in the interpretation of encoding and decoding models trained on neuroimaging data. In this article, we investigate which causal statements are warranted and which ones are not supported by empirical evidence. We argue that the distinction between encoding and decoding models is not sufficient for this purpose: relevant features in encoding and decoding models carry a different meaning in stimulus- and in response-based experimental paradigms.We show that only encoding models in the stimulus-based setting support unambiguous causal interpretations. By combining encoding and decoding models trained on the same data, however, we obtain insights into causal relations beyond those that are implied by each individual model type. We illustrate the empirical relevance of our theoretical findings on EEG data recorded during a visuo-motor learning task. PMID:25623501

  6. Causal structure in categorical quantum mechanics

    NASA Astrophysics Data System (ADS)

    Lal, Raymond Ashwin

    Categorical quantum mechanics is a way of formalising the structural features of quantum theory using category theory. It uses compound systems as the primitive notion, which is formalised by using symmetric monoidal categories. This leads to an elegant formalism for describing quantum protocols such as quantum teleportation. In particular, categorical quantum mechanics provides a graphical calculus that exposes the information flow of such protocols in an intuitive way. However, the graphical calculus also reveals surprising features of these protocols; for example, in the quantum teleportation protocol, information appears to flow `backwards-in-time'. This leads to question of how causal structure can be described within categorical quantum mechanics, and how this might lead to insight regarding the structural compatibility between quantum theory and relativity. This thesis is concerned with the project of formalising causal structure in categorical quantum mechanics. We begin by studying an abstract view of Bell-type experiments, as described by `no-signalling boxes', and we show that under time-reversal no-signalling boxes generically become signalling. This conflicts with the underlying symmetry of relativistic causal structure. This leads us to consider the framework of categorical quantum mechanics from the perspective of relativistic causal structure. We derive the properties that a symmetric monoidal category must satisfy in order to describe systems in such a background causal structure. We use these properties to define a new type of category, and this provides a formal framework for describing protocols in spacetime. We explore this new structure, showing how it leads to an understanding of the counter-intuitive information flow of protocols in categorical quantum mechanics. We then find that the formal properties of our new structure are naturally related to axioms for reconstructing quantum theory, and we show how a reconstruction scheme based on

  7. 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

  8. 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

  9. On the concept of Bell’s local causality in local classical and quantum theory

    SciTech Connect

    Hofer-Szabó, Gábor; Vecsernyés, Péter

    2015-03-15

    The aim of this paper is to implement Bell’s notion of local causality into a framework, called local physical theory. This framework, based on the axioms of algebraic field theory, is broad enough to integrate both probabilistic and spatiotemporal concepts and also classical and quantum theories. Bell’s original idea of local causality will arise as the classical case of our definition. Classifying local physical theories by whether they obey local primitive causality, a property rendering the dynamics of the theory causal, we then investigate what is needed for a local physical theory to be locally causal. Finally, comparing local causality with the common cause principles and relating both to the Bell inequalities we find a nice parallelism: Bell inequalities cannot be derived neither from local causality nor from a common cause unless the local physical theory is classical or the common cause is commuting, respectively.

  10. Rethinking temporal contiguity and the judgement of causality: effects of prior knowledge, experience, and reinforcement procedure.

    PubMed

    Buehner, Marc J; May, Jon

    2003-07-01

    Time plays a pivotal role in causal inference. Nonetheless most contemporary theories of causal induction do not address the implications of temporal contiguity and delay, with the exception of associative learning theory. Shanks, Pearson, and Dickinson (1989) and several replications (Reed, 1992, 1999) have demonstrated that people fail to identify causal relations if cause and effect are separated by more than two seconds. In line with an associationist perspective, these findings have been interpreted to indicate that temporal lags universally impair causal induction. This interpretation clashes with the richness of everyday causal cognition where people apparently can reason about causal relations involving considerable delays. We look at the implications of cause-effect delays from a computational perspective and predict that delays should generally hinder reasoning performance, but that this hindrance should be alleviated if reasoners have knowledge of the delay. Two experiments demonstrated that (1) the impact of delay on causal judgement depends on participants' expectations about the timeframe of the causal relation, and (2) the free-operant procedures used in previous studies are ill-suited to study the direct influences of delay on causal induction, because they confound delay with weaker evidence for the relation in question. Implications for contemporary causal learning theories are discussed.

  11. Nonlinear connectivity by Granger causality.

    PubMed

    Marinazzo, Daniele; Liao, Wei; Chen, Huafu; Stramaglia, Sebastiano

    2011-09-15

    The communication among neuronal populations, reflected by transient synchronous activity, is the mechanism underlying the information processing in the brain. Although it is widely assumed that the interactions among those populations (i.e. functional connectivity) are highly nonlinear, the amount of nonlinear information transmission and its functional roles are not clear. The state of the art to understand the communication between brain systems are dynamic causal modeling (DCM) and Granger causality. While DCM models nonlinear couplings, Granger causality, which constitutes a major tool to reveal effective connectivity, and is widely used to analyze EEG/MEG data as well as fMRI signals, is usually applied in its linear version. In order to capture nonlinear interactions between even short and noisy time series, a few approaches have been proposed. We review them and focus on a recently proposed flexible approach has been recently proposed, consisting in the kernel version of Granger causality. We show the application of the proposed approach on EEG signals and fMRI data.

  12. 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.

  13. Causal Inference for a Population of Causally Connected Units

    PubMed Central

    van der Laan, Mark J.

    2015-01-01

    Suppose that we observe a population of causally connected units. On each unit at each time-point on a grid we observe a set of other units the unit is potentially connected with, and a unit-specific longitudinal data structure consisting of baseline and time-dependent covariates, a time-dependent treatment, and a final outcome of interest. The target quantity of interest is defined as the mean outcome for this group of units if the exposures of the units would be probabilistically assigned according to a known specified mechanism, where the latter is called a stochastic intervention. Causal effects of interest are defined as contrasts of the mean of the unit-specific outcomes under different stochastic interventions one wishes to evaluate. This covers a large range of estimation problems from independent units, independent clusters of units, and a single cluster of units in which each unit has a limited number of connections to other units. The allowed dependence includes treatment allocation in response to data on multiple units and so called causal interference as special cases. We present a few motivating classes of examples, propose a structural causal model, define the desired causal quantities, address the identification of these quantities from the observed data, and define maximum likelihood based estimators based on cross-validation. In particular, we present maximum likelihood based super-learning for this network data. Nonetheless, such smoothed/regularized maximum likelihood estimators are not targeted and will thereby be overly bias w.r.t. the target parameter, and, as a consequence, generally not result in asymptotically normally distributed estimators of the statistical target parameter. To formally develop estimation theory, we focus on the simpler case in which the longitudinal data structure is a point-treatment data structure. We formulate a novel targeted maximum likelihood estimator of this estimand and show that the double robustness of the

  14. 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.

  15. Effects of question formats on causal judgments and model evaluation

    PubMed Central

    Smithson, Michael

    2015-01-01

    Evaluation of causal reasoning models depends on how well the subjects’ causal beliefs are assessed. Elicitation of causal beliefs is determined by the experimental questions put to subjects. We examined the impact of question formats commonly used in causal reasoning research on participant’s responses. The results of our experiment (Study 1) demonstrate that both the mean and homogeneity of the responses can be substantially influenced by the type of question (structure induction versus strength estimation versus prediction). Study 2A demonstrates that subjects’ responses to a question requiring them to predict the effect of a candidate cause can be significantly lower and more heterogeneous than their responses to a question asking them to diagnose a cause when given an effect. Study 2B suggests that diagnostic reasoning can strongly benefit from cues relating to temporal precedence of the cause in the question. Finally, we evaluated 16 variations of recent computational models and found the model fitting was substantially influenced by the type of questions. Our results show that future research in causal reasoning should place a high priority on disentangling the effects of question formats from the effects of experimental manipulations, because that will enable comparisons between models of causal reasoning uncontaminated by method artifact. PMID:25954225

  16. Effects of question formats on causal judgments and model evaluation.

    PubMed

    Shou, Yiyun; Smithson, Michael

    2015-01-01

    Evaluation of causal reasoning models depends on how well the subjects' causal beliefs are assessed. Elicitation of causal beliefs is determined by the experimental questions put to subjects. We examined the impact of question formats commonly used in causal reasoning research on participant's responses. The results of our experiment (Study 1) demonstrate that both the mean and homogeneity of the responses can be substantially influenced by the type of question (structure induction versus strength estimation versus prediction). Study 2A demonstrates that subjects' responses to a question requiring them to predict the effect of a candidate cause can be significantly lower and more heterogeneous than their responses to a question asking them to diagnose a cause when given an effect. Study 2B suggests that diagnostic reasoning can strongly benefit from cues relating to temporal precedence of the cause in the question. Finally, we evaluated 16 variations of recent computational models and found the model fitting was substantially influenced by the type of questions. Our results show that future research in causal reasoning should place a high priority on disentangling the effects of question formats from the effects of experimental manipulations, because that will enable comparisons between models of causal reasoning uncontaminated by method artifact.

  17. 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

  18. Suppression of male-specific cytochrome P450 2c and its mRNA by 3,4,5,3',4',5'-hexachlorobiphenyl in rat liver is not causally related to changes in serum testosterone.

    PubMed

    Yeowell, H N; Waxman, D J; LeBlanc, G A; Linko, P; Goldstein, J A

    1989-06-01

    Rat cytochrome P450 2c (P450 gene IIC11) is a constitutive, male-specific hepatic enzyme which is suppressed greater than 90% by treatment with 3,4,5,3',4',5'-hexachlorobiphenyl (HCB) [H. N. Yeowell et al. (1987) Mol. Pharmacol. 32, 340-347]. HCB also decreases serum testosterone levels in adult male rats (greater than 98% loss). The present study assesses whether the suppression of P450 2c by HCB is a direct result of its effects on serum testosterone levels. Further, the site along the hypothalamic-pituitary-testicular axis at which HCB acts to depress testosterone secretion was examined. Administration of the synthetic androgen methyltrienolone to HCB-treated rats failed to prevent the suppression of P450 2c mRNA and its associated microsomal steroid 16 alpha-hydroxylase activity under conditions where it effectively reversed the large decrease in P450 2c mRNA and steroid 16 alpha-hydroxylase activity produced by castration. Hepatic steroid 6 beta-hydroxylase activity, which is catalyzed primarily by P450 2a (P450 gene IIIA2), was also suppressed by HCB and was not protected by methyltrienolone. Administration of either human chorionic gonadotropin, an analog of pituitary-derived luteinizing hormone, or the hypothalamic luteinizing hormone releasing hormone elevated serum testosterone levels to a much smaller extent in HCB-treated rats than in control rats. These results indicate that the effects of HCB on serum testosterone levels reflect its effects on testicular function rather than the pituitary or hypothalamus. However, the present study demonstrates that the consequential reduction in serum testosterone levels in HCB-treated rats is not causally related to the reduction in hepatic P450 2c levels. Thus, HCB must also act on some other regulatory mechanism involved in the expression of this protein.

  19. Regulatory causality evaluation methods applied in kava hepatotoxicity: are they appropriate?

    PubMed

    Teschke, Rolf; Wolff, Albrecht

    2011-02-01

    Since 1998 liver injury has been assumed in some patients after the use of kava (Piper methysticum G. Forster) as an anxyolytic herbal extract, but the regulatory causality evaluation of these cases was a matter of international and scientific debate. This review critically analyzes the regulatory issues of causality assessments of patients with primarily suspected kava hepatotoxicity and suggests recommendations for minimizing regulatory risks when assessing causality in these and other related cases. The various regulatory causality approaches were based on liver unspecific assessments such as ad hoc evaluations, the WHO scale using the definitions of the WHO Collaborating Centre for International Drug Monitoring, and the Naranjo scale. Due to their liver unspecificity, however, these causality approaches are not suitable for assessing cases of primarily assumed liver related adverse reactions by drugs and herbs including kava. Major problems emerged trough the combination of regulatory inappropriate causality assessment methods with the poor data quality as presented by the regulatory agency when reassessment was done and the resulting data were heavily criticized worldwide within the scientific community. Conversely, causality of cases with primarily assumed kava hepatotoxicity is best assessed by structured, quantitative and liver specific causality algorithms such as the scale of the CIOMS (Council for International Organizations of Medical Sciences) or the main-test as its update. Future strategies should therefore focus on the implementation of structured, quantitative and liver specific causality assessment methods as regulatory standards to improve regulatory causality assessments for liver injury by drugs and herbs including kava.

  20. 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…

  1. Expectations and interpretations during causal learning.

    PubMed

    Luhmann, Christian C; Ahn, Woo-Kyoung

    2011-05-01

    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 expectations developed during causal learning, learners give varied interpretations to covariation information as it is encountered and that these interpretations influence the resulting causal beliefs. In Experiments 1A-1C, participants' interpretations of observations during a causal learning task were dynamic, expectation based, and, furthermore, strongly tied to subsequent causal judgments. Experiment 2 demonstrated that adding trials of joint absence or joint presence of events, whose roles have been traditionally interpreted as increasing causal strengths, could result in decreased overall causal judgments and that adding trials where one event occurs in the absence of another, whose roles have been traditionally interpreted as decreasing causal strengths, could result in increased overall causal judgments. We discuss implications for traditional models of causal learning and how a more top-down approach (e.g., Bayesian) would be more compatible with the current findings.

  2. 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…

  3. Expectations and Interpretations During Causal Learning

    PubMed Central

    Luhmann, Christian C.; Ahn, Woo-kyoung

    2012-01-01

    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 expectations developed during causal learning, learners give varied interpretations to covariation information as it is encountered and that these interpretations influence the resulting causal beliefs. In Experiments 1A–1C, participants’ interpretations of observations during a causal learning task were dynamic, expectation based, and, furthermore, strongly tied to subsequent causal judgments. Experiment 2 demonstrated that adding trials of joint absence or joint presence of events, whose roles have been traditionally interpreted as increasing causal strengths, could result in decreased overall causal judgments and that adding trials where one event occurs in the absence of another, whose roles have been traditionally interpreted as decreasing causal strengths, could result in increased overall causal judgments. We discuss implications for traditional models of causal learning and how a more top-down approach (e.g., Bayesian) would be more compatible with the current findings. PMID:21534705

  4. 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 inferences, and…

  5. 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…

  6. 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.…

  7. Causal inference in economics and marketing.

    PubMed

    Varian, Hal R

    2016-07-01

    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.

  8. 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…

  9. Modeling of causality with metamaterials

    NASA Astrophysics Data System (ADS)

    Smolyaninov, Igor I.

    2013-02-01

    Hyperbolic metamaterials may be used to model a 2 + 1-dimensional Minkowski space-time in which the role of time is played by one of the spatial coordinates. When a metamaterial is built and illuminated with a coherent extraordinary laser beam, the stationary pattern of light propagation inside the metamaterial may be treated as a collection of particle world lines, which represents a complete ‘history’ of this 2 + 1-dimensional space-time. While this model may be used to build interesting space-time analogs, such as metamaterial ‘black holes’ and a metamaterial ‘big bang’, it lacks causality: since light inside the metamaterial may propagate back and forth along the ‘timelike’ spatial coordinate, events in the ‘future’ may affect events in the ‘past’. Here we demonstrate that a more sophisticated metamaterial model may fix this deficiency via breaking the mirror and temporal (PT) symmetries of the original model and producing one-way propagation along the ‘timelike’ spatial coordinate. The resulting 2 + 1-dimensional Minkowski space-time appears to be causal. This scenario may be considered as a metamaterial model of the Wheeler-Feynman absorber theory of causality.

  10. Velocity Requirements for Causality Violation

    NASA Astrophysics Data System (ADS)

    Modanese, Giovanni

    We re-examine the "Regge-Tolman paradox" with reference to some recent experimental results. It is straightforward to find a formula for the velocity v of the moving system required to produce causality violation. This formula typically yields a velocity very close to the speed of light (for instance, v/c > 0.97 for X-shaped microwaves), which raises some doubts about the real physical observability of the violations. We then compute the velocity requirement introducing a delay between the reception of the primary signal and the emission of the secondary. It turns out that in principle for any delay it is possible to find moving observers able to produce active causal violation. This is mathematically due to the singularity of the Lorentz transformations for β →1. For a realistic delay due to the propagation of a luminal precursor, we find that causality violations in the reported experiments are still more unlikely (v/c > 0.989), and even in the hypothesis that the superluminal propagation velocity goes to infinity, the velocity requirement is bounded by v/c > 0.62. We also prove that if two oscopic bodies exchange energy and momentum through superluminal signals, then the swap of signal source and target is incompatible with the Lorentz transformations; therefore it is not possible to distinguish between source and target, even with reference to a definite reference frame.

  11. Causal Attributions and Recovery from Rape: Implications for Counseling.

    ERIC Educational Resources Information Center

    Frazier, Patricia A.; Schauben, Laura J.

    One factor related to postrape trauma is the survivor's belief about the cause of the rape. Most research to date on the relation between causal attributions and postrape recovery has been guided by a theoretical model which proposes that certain types of self-blame can be adaptive for survivors. Specifically, behavioral self-blame is thought to…

  12. Neuroleptic malignant syndrome: mechanisms, interactions, and causality.

    PubMed

    Gillman, P Ken

    2010-09-15

    This review focuses on new data from recent publications concerning how compounding interactions between different thermoregulatory pathways influence the development of hyperthermia and/or neuroleptic malignant syndrome (NMS), and the fundamental issue of the presumed causal role of antipsychotic drugs. The formal criteria for substantiating cause-effect relationships in medical science, established by Hill, are applied to NMS and, for comparison, also to malignant hyperthermia and serotonin toxicity. The risk of morbidities related to hyperthermia is reviewed from human and experimental data: temperatures in excess of 39.5°C cause physiological and cellular dysfunction and high mortality. The most temperature-sensitive elements of neural cells are mitochondrial and plasma membranes, in which irreversible changes occur around 40°C. Temperatures of up to 39°C are "normal" in mammals, so, the term hyperthermia should be reserved for temperatures of 39.5°C or greater. The implicitly accepted presumption that NMS is a hypermetabolic and hyperthermic syndrome is questionable and does not explain the extensive morbidity in the majority of cases, where the temperature is less than 39°C. The thermoregulatory effects of dopamine and acetylcholine are outlined, especially because they are probably the main pathways by which neuroleptic drugs might affect thermoregulation. It is notable that even potent antagonism of these mechanisms rarely causes temperature elevation and that multiple mechanisms, including the acute phase response, stress-induced hyperthermia, drugs effects, etc., involving compounding interactions, are required to precipitate hyperthermia. The application of the Hill criteria clearly supports causality for drugs inducing both MH and ST but do not support causality for NMS.

  13. Voluntary action and causality in temporal binding.

    PubMed

    Cravo, Andre M; Claessens, Peter M E; Baldo, Marcus V C

    2009-10-01

    Previous studies have documented temporal attraction in perceived times of actions and their effects. While some authors argue that voluntary action is a necessary condition for this phenomenon, others claim that the causal relationship between action and effect is the crucial ingredient. In the present study, we investigate voluntary action and causality as the necessary and sufficient conditions for temporal binding. We used a variation of the launching effect proposed by Michotte, in which participants controlled the launch stimulus in some blocks. Volunteers reported causality ratings and estimated the interval between the two events. Our results show dissociations between causality ratings and temporal estimation. While causality ratings are not affected by voluntary action, temporal bindings were only found in the presence of both voluntary action and high causality. Our results indicate that voluntary action and causality are both necessary for the emergence of temporal binding.

  14. Conditional Granger causality and partitioned Granger causality: differences and similarities.

    PubMed

    Malekpour, Sheida; Sethares, William A

    2015-12-01

    Neural information modeling and analysis often requires a measurement of the mutual influence among many signals. A common technique is the conditional Granger causality (cGC) which measures the influence of one time series on another time series in the presence of a third. Geweke has translated this condition into the frequency domain and has explored the mathematical relationships between the time and frequency domain expressions. Chen has observed that in practice, the expressions may return (meaningless) negative numbers, and has proposed an alternative which is based on a partitioned matrix scheme, which we call partitioned Granger causality (pGC). There has been some confusion in the literature about the relationship between cGC and pGC; some authors treat them as essentially identical measures, while others have noted that some properties (such as the relationship between the time and frequency domain expressions) do not hold for the pGC. This paper presents a series of matrix equalities that simplify the calculation of the pGC. In this simplified expression, the essential differences and similarities between the cGC and the pGC become clear; in essence, the pGC is dependent on only a subset of the parameters in the model estimation, and the noise residuals (which are uncorrelated in the cGC) need not be uncorrelated in the pGC. The mathematical results are illustrated with a simulation, and the measures are applied to an EEG dataset.

  15. Algorithms for the inference of causality in dynamic processes: Application to cardiovascular and cerebrovascular variability.

    PubMed

    Faes, Luca; Porta, Alberto; Nollo, Giandomenico

    2015-08-01

    This study faces the problem of causal inference in multivariate dynamic processes, with specific regard to the detection of instantaneous and time-lagged directed interactions. We point out the limitations of the traditional Granger causality analysis, showing that it leads to false detection of causality when instantaneous and time-lagged effects coexist in the process structure. Then, we propose an improved algorithm for causal inference that combines the Granger framework with the approach proposed by Pearl for the study of causality among multiple random variables. This new approach is compared with the traditional one in theoretical and simulated examples of interacting processes, showing its ability to retrieve the correct structure of instantaneous and time-lagged interactions. These approaches for causal inference are then tested on the physiological variability series of heart period, arterial pressure and cerebral blood flow variability obtained in subjects with postural-related syncope during a tilt-test protocol.

  16. Immunity in arterial hypertension: associations or causalities?

    PubMed

    Anders, Hans-Joachim; Baumann, Marcus; Tripepi, Giovanni; Mallamaci, Francesca

    2015-12-01

    Numerous studies describe associations between markers of inflammation and arterial hypertension (aHT), but does that imply causality? Interventional studies that reduce blood pressure reduced also markers of inflammation, but does immunosuppression improve hypertension? Here, we review the available mechanistic data. Aberrant immunity can trigger endothelial dysfunction but is hardly ever the primary cause of aHT. Innate and adaptive immunity get involved once hypertension has caused vascular wall injury as immunity is a modifier of endothelial dysfunction and vascular wall remodelling. As vascular remodelling progresses, immunity-related mechanisms can become significant cofactors for cardiovascular (CV) disease progression; vice versa, suppressing immunity can improve hypertension and CV outcomes. Innate and adaptive immunity both contribute to vascular wall remodelling. Innate immunity is driven by danger signals that activate Toll-like receptors and other pattern-recognition receptors. Adaptive immunity is based on loss of tolerance against vascular autoantigens and includes autoreactive T-cell immunity as well as non-HLA angiotensin II type 1 receptor-activating autoantibodies. Such processes involve numerous other modulators such as regulatory T cells. Together, immunity is not causal for hypertension but rather an important secondary pathomechanism and a potential therapeutic target in hypertension.

  17. Scalar Fields via Causal Tapestries

    NASA Astrophysics Data System (ADS)

    Sulis, William

    2012-02-01

    Causal tapestries provide a framework for implementing an explicit Process Theory approach to quantum foundations which models information flow within a physical system. We consider event-transition tapestry pairs. An event tapestry O is a 4-tuple (L, K, M, Ip ) where K is an index set of cardinality κ, M = M x F(M) x D x P(M') a mathematical structure with M a causal space, F(M) a function space, D a descriptor space, P(M') either a Lie algebra or tangent space on a manifold M', Ip an event tapestry. L consists of elements of the form [n]<α>G, n in K, α in M and G an acyclic directed graph whose vertex set is a subset of Lp Likewise, a transition tapestry π is a 4-tuple (L', K', M', I'p ) where M' = M' x F(M') x D' x P'(M). The dynamic generates a consistent succession of O-π pairs by means of a game based on the technique of forcing used in logic to generate models. This dynamic has previously been shown to be compatible with Lorentz invariance. An application of this approach to model scalar fields is presented in which each informon is associated with a function of the form f(πk1 /σ1 ,,πkN /σN )sin ( σ1 t1 --πk1 )/ ( σ1 t1 --πk1 ) .sin ( σN tN --πkN )/ ( σN tN --πkN ) and the WSK interpolation theorem is used to generate the resulting scalar field on the causal manifold.

  18. Space and time in perceptual causality.

    PubMed

    Straube, Benjamin; Chatterjee, Anjan

    2010-01-01

    Inferring causality is a fundamental feature of human cognition that allows us to theorize about and predict future states of the world. Michotte suggested that humans automatically perceive causality based on certain perceptual features of events. However, individual differences in judgments of perceptual causality cast doubt on Michotte's view. To gain insights in the neural basis of individual difference in the perception of causality, our participants judged causal relationships in animations of a blue ball colliding with a red ball (a launching event) while fMRI-data were acquired. Spatial continuity and temporal contiguity were varied parametrically in these stimuli. We did not find consistent brain activation differences between trials judged as caused and those judged as non-caused, making it unlikely that humans have universal instantiation of perceptual causality in the brain. However, participants were slower to respond to and showed greater neural activity for violations of causality, suggesting that humans are biased to expect causal relationships when moving objects appear to interact. Our participants demonstrated considerable individual differences in their sensitivity to spatial and temporal characteristics in perceiving causality. These qualitative differences in sensitivity to time or space in perceiving causality were instantiated in individual differences in activation of the left basal ganglia or right parietal lobe, respectively. Thus, the perception that the movement of one object causes the movement of another is triggered by elemental spatial and temporal sensitivities, which themselves are instantiated in specific distinct neural networks. PMID:20463866

  19. Children's use of interventions to learn causal structure.

    PubMed

    McCormack, Teresa; Bramley, Neil; Frosch, Caren; Patrick, Fiona; Lagnado, David

    2016-01-01

    Children between 5 and 8 years of age freely intervened on a three-variable causal system, with their task being to discover whether it was a common cause structure or one of two causal chains. From 6 or 7 years of age, children were able to use information from their interventions to correctly disambiguate the structure of a causal chain. We used a Bayesian model to examine children's interventions on the system; this showed that with development children became more efficient in producing the interventions needed to disambiguate the causal structure and that the quality of interventions, as measured by their informativeness, improved developmentally. The latter measure was a significant predictor of children's correct inferences about the causal structure. A second experiment showed that levels of performance were not reduced in a task where children did not select and carry out interventions themselves, indicating no advantage for self-directed learning. However, children's performance was not related to intervention quality in these circumstances, suggesting that children learn in a different way when they carry out interventions themselves. PMID:26298433

  20. Quantifying causal emergence shows that macro can beat micro.

    PubMed

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

    2013-12-01

    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.

  1. The impact of domain-specific beliefs on decisions and causal judgments.

    PubMed

    Müller, S M; Garcia-Retamero, R; Galesic, M; Maldonado, A

    2013-11-01

    Extensive evidence suggests that people often rely on their causal beliefs in their decisions and causal judgments. To date, however, there is a dearth of research comparing the impact of causal beliefs in different domains. We conducted two experiments to map the influence of domain-specific causal beliefs on the evaluation of empirical evidence when making decisions and subsequent causal judgments. Participants made 120 decisions in a two-alternative forced-choice task, framed in either a medical or a financial domain. Before each decision, participants could actively search for information about the outcome ("occurrence of a disease" or "decrease in a company's share price") on the basis of four cues. To analyze the strength of causal beliefs, we set two cues to have a generative relation to the outcome and two to have a preventive relation to the outcome. To examine the influence of empirical evidence, we manipulated the predictive power (i.e., cue validities) of the cues. Both experiments included a validity switch, where the four selectable cues switched from high to low validity or vice versa. Participants had to make a causal judgment about each cue before and after the validity switch. In the medical domain, participants stuck to the causal information in causal judgments, even when evidence was contradictory, while decisions showed an effect of both empirical and causal information. In contrast, in the financial domain, participants mainly adapted their decisions and judgments to the cue validities. We conclude that the strength of causal beliefs (1) is shaped by the domain, and (2) has a differential influence on the degree to which empirical evidence is taken into account in causal judgments and decision making.

  2. There aren't plenty more fish in the sea: a causal network approach.

    PubMed

    Nikolic, Milena; Lagnado, David A

    2015-11-01

    The current research investigated how lay representations of the causes of an environmental problem may underlie individuals' reasoning about the issue. Naïve participants completed an experiment that involved two main tasks. The causal diagram task required participants to depict the causal relations between a set of factors related to overfishing and to estimate the strength of these relations. The counterfactual task required participants to judge the effect of counterfactual suppositions based on the diagrammed factors. We explored two major questions: (1) what is the relation between individual causal models and counterfactual judgments? Consistent with previous findings (e.g., Green et al., 1998, Br. J. Soc. Psychology, 37, 415), these judgments were best explained by a combination of the strength of both direct and indirect causal paths. (2) To what extent do people use two-way causal thinking when reasoning about an environmental problem? In contrast to previous research (e.g., White, 2008, Appl. Cogn. Psychology, 22, 559), analyses based on individual causal networks revealed the presence of numerous feedback loops. The studies support the value of analysing individual causal models in contrast to consensual representations. Theoretical and practical implications are discussed in relation to causal reasoning as well as environmental psychology.

  3. There aren't plenty more fish in the sea: a causal network approach.

    PubMed

    Nikolic, Milena; Lagnado, David A

    2015-11-01

    The current research investigated how lay representations of the causes of an environmental problem may underlie individuals' reasoning about the issue. Naïve participants completed an experiment that involved two main tasks. The causal diagram task required participants to depict the causal relations between a set of factors related to overfishing and to estimate the strength of these relations. The counterfactual task required participants to judge the effect of counterfactual suppositions based on the diagrammed factors. We explored two major questions: (1) what is the relation between individual causal models and counterfactual judgments? Consistent with previous findings (e.g., Green et al., 1998, Br. J. Soc. Psychology, 37, 415), these judgments were best explained by a combination of the strength of both direct and indirect causal paths. (2) To what extent do people use two-way causal thinking when reasoning about an environmental problem? In contrast to previous research (e.g., White, 2008, Appl. Cogn. Psychology, 22, 559), analyses based on individual causal networks revealed the presence of numerous feedback loops. The studies support the value of analysing individual causal models in contrast to consensual representations. Theoretical and practical implications are discussed in relation to causal reasoning as well as environmental psychology. PMID:25597224

  4. Causality as individual essence: its bearing on synchronicity.

    PubMed

    Tougas, Cecile T

    2014-06-01

    Causality, time, and number are subjectively lived realities and need to be noticed as such. Fundamental to the wide range of living experience, they are also basic to scientific knowing. In this article I examine causality in relation to an article on synchronicity by Harald Atmanspacher and Wolfgang Fach. My examination is neither scientific nor metaphysical, but rather phenomenological, as it is a clarification of form as individual essence of a thing. This non-material form of an individual thing in the widest sense of the word 'thing' was rejected and so lost during modern seventeenth-century science but, renewed now, can help describe synchronicity. A commentary by William Willeford follows.

  5. 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.

  6. Causal diagrams in systems epidemiology

    PubMed Central

    2012-01-01

    Methods of diagrammatic modelling have been greatly developed in the past two decades. Outside the context of infectious diseases, systematic use of diagrams in epidemiology has been mainly confined to the analysis of a single link: that between a disease outcome and its proximal determinant(s). Transmitted causes ("causes of causes") tend not to be systematically analysed. The infectious disease epidemiology modelling tradition models the human population in its environment, typically with the exposure-health relationship and the determinants of exposure being considered at individual and group/ecological levels, respectively. Some properties of the resulting systems are quite general, and are seen in unrelated contexts such as biochemical pathways. Confining analysis to a single link misses the opportunity to discover such properties. The structure of a causal diagram is derived from knowledge about how the world works, as well as from statistical evidence. A single diagram can be used to characterise a whole research area, not just a single analysis - although this depends on the degree of consistency of the causal relationships between different populations - and can therefore be used to integrate multiple datasets. Additional advantages of system-wide models include: the use of instrumental variables - now emerging as an important technique in epidemiology in the context of mendelian randomisation, but under-used in the exploitation of "natural experiments"; the explicit use of change models, which have advantages with respect to inferring causation; and in the detection and elucidation of feedback. PMID:22429606

  7. 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…

  8. 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

  9. Quantum field theory and gravity in causal sets

    NASA Astrophysics Data System (ADS)

    Sverdlov, Roman M.

    Causal set is a model of space time that allows to reconcile discreteness and manifest relativistic invariance. This is done by viewing space time as finite, partially ordered set. The elements of the set are viewed as points of space time, or events; the partial ordering between them is viewed as causal relations. It has been shown that, in discrete scenario, the information about causal relations between events can, indeed, approximate the metric. The goal of this thesis is to introduce matter fields and their Lagrangians into causal set context. This is a two step process. The first step is to re-define gauge fields, gravity, and distances in such a way that no reference to Lorentz index is made. This is done by defining gauge fields as two-point real valued functions, and gravitational field as causal structure itself. Once the above is done, Lagrangians have to be defined in a way that they don't refer to Lorentzian indices either. This is done by introducing a notion of type 1 and type 2 Lagrangian generators, coupled with respective machinery that "translates" each generator into corresponding Lagrangian. The fields that are subject to these generators are, respectively, defined as type 1 and type 2. The main difference between two kinds of fields is the prediction of different behavior in different dimensions of type 2 fields. However, despite our inability to travel to different dimensions, gravity was shown to be type 2 based on the erroneous predictions of its 4-dimensional behavior if it was viewed as type 1. However, no erroneous predictions are made if non-gravitational fields are viewed as either type 1 or type 2, thus the nature of the latter is still an open question. Finally, an attempt was made to provide interpretation of quantum mechanics that would allow to limit fluctuations of causal structure to allow some topological background. However, due to its controversial nature, it is placed in the Appendix.

  10. 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…

  11. 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 Batesonian…

  12. The Relationship between Battered Women's Causal Attributions for Violence and Coping Efforts

    ERIC Educational Resources Information Center

    Meyer, Alicia; Wagner, Barry; Dutton, Mary Ann

    2010-01-01

    This study investigates the relationship between battered women's causal attributions for the violence they experience and their subsequent coping efforts. Causal attributions related to partner blame, excusing the violence, and the combination of partner blame and excusing the violence were regressed on six categories of coping strategies:…

  13. 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

  14. Nonparametric causal inference for bivariate time series.

    PubMed

    McCracken, James M; Weigel, Robert S

    2016-02-01

    We introduce new quantities for exploratory causal inference between bivariate time series. The quantities, called penchants and leanings, are computationally straightforward to apply, follow directly from assumptions of probabilistic causality, do not depend on any assumed models for the time series generating process, and do not rely on any embedding procedures; these features may provide a clearer interpretation of the results than those from existing time series causality tools. The penchant and leaning are computed based on a structured method for computing probabilities.

  15. Physical integration: a causal account for consciousness.

    PubMed

    Manzotti, Riccardo; Chella, Antonio

    2014-06-01

    The issue of integration in neural networks is intimately connected with that of consciousness. In this paper, integration as an effective level of physical organization is contrasted with a methodological integrative approach. Understanding how consciousness arises out of neural processes requires a model of integration in just causal physical terms. Based on a set of feasible criteria (physical grounding, causal efficacy, no circularity and scaling), a causal account of physical integration for consciousness centered on joint causation is outlined.

  16. 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

  17. Causal inference in economics and marketing.

    PubMed

    Varian, Hal R

    2016-07-01

    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

  18. Nonparametric causal inference for bivariate time series

    NASA Astrophysics Data System (ADS)

    McCracken, James M.; Weigel, Robert S.

    2016-02-01

    We introduce new quantities for exploratory causal inference between bivariate time series. The quantities, called penchants and leanings, are computationally straightforward to apply, follow directly from assumptions of probabilistic causality, do not depend on any assumed models for the time series generating process, and do not rely on any embedding procedures; these features may provide a clearer interpretation of the results than those from existing time series causality tools. The penchant and leaning are computed based on a structured method for computing probabilities.

  19. Algorithms of causal inference for the analysis of effective connectivity among brain regions

    PubMed Central

    Chicharro, Daniel; Panzeri, Stefano

    2014-01-01

    In recent years, powerful general algorithms of causal inference have been developed. In particular, in the framework of Pearl’s causality, algorithms of inductive causation (IC and IC*) provide a procedure to determine which causal connections among nodes in a network can be inferred from empirical observations even in the presence of latent variables, indicating the limits of what can be learned without active manipulation of the system. These algorithms can in principle become important complements to established techniques such as Granger causality and Dynamic Causal Modeling (DCM) to analyze causal influences (effective connectivity) among brain regions. However, their application to dynamic processes has not been yet examined. Here we study how to apply these algorithms to time-varying signals such as electrophysiological or neuroimaging signals. We propose a new algorithm which combines the basic principles of the previous algorithms with Granger causality to obtain a representation of the causal relations suited to dynamic processes. Furthermore, we use graphical criteria to predict dynamic statistical dependencies between the signals from the causal structure. We show how some problems for causal inference from neural signals (e.g., measurement noise, hemodynamic responses, and time aggregation) can be understood in a general graphical approach. Focusing on the effect of spatial aggregation, we show that when causal inference is performed at a coarser scale than the one at which the neural sources interact, results strongly depend on the degree of integration of the neural sources aggregated in the signals, and thus characterize more the intra-areal properties than the interactions among regions. We finally discuss how the explicit consideration of latent processes contributes to understand Granger causality and DCM as well as to distinguish functional and effective connectivity. PMID:25071541

  20. Algorithms of causal inference for the analysis of effective connectivity among brain regions.

    PubMed

    Chicharro, Daniel; Panzeri, Stefano

    2014-01-01

    In recent years, powerful general algorithms of causal inference have been developed. In particular, in the framework of Pearl's causality, algorithms of inductive causation (IC and IC(*)) provide a procedure to determine which causal connections among nodes in a network can be inferred from empirical observations even in the presence of latent variables, indicating the limits of what can be learned without active manipulation of the system. These algorithms can in principle become important complements to established techniques such as Granger causality and Dynamic Causal Modeling (DCM) to analyze causal influences (effective connectivity) among brain regions. However, their application to dynamic processes has not been yet examined. Here we study how to apply these algorithms to time-varying signals such as electrophysiological or neuroimaging signals. We propose a new algorithm which combines the basic principles of the previous algorithms with Granger causality to obtain a representation of the causal relations suited to dynamic processes. Furthermore, we use graphical criteria to predict dynamic statistical dependencies between the signals from the causal structure. We show how some problems for causal inference from neural signals (e.g., measurement noise, hemodynamic responses, and time aggregation) can be understood in a general graphical approach. Focusing on the effect of spatial aggregation, we show that when causal inference is performed at a coarser scale than the one at which the neural sources interact, results strongly depend on the degree of integration of the neural sources aggregated in the signals, and thus characterize more the intra-areal properties than the interactions among regions. We finally discuss how the explicit consideration of latent processes contributes to understand Granger causality and DCM as well as to distinguish functional and effective connectivity.

  1. Comparative performance evaluation of data-driven causality measures applied to brain networks.

    PubMed

    Fasoula, Angie; Attal, Yohan; Schwartz, Denis

    2013-05-15

    In this article, several well-known data-driven causality methods are revisited and comparatively evaluated. These are the Granger-Geweke Causality (GGC), the Partial Directed Coherence (PDC), the Directed Transfer Function (DTF) and the Direct Directed Transfer Function (dDTF). The robustness of the four causality measures against two degradation factors is quantitatively evaluated. These are: the presence of realistic biological/electronic noise at various SNR levels, as recorded on a MagnetoEncephalography (MEG) machine, and the presence of a weak node in the brain network where the causality analysis is applied. The causality measures are evaluated in terms of the relative estimation error and the compromise between true and fictitious causal density in the brain network. Both parametric and non-parametric causality analysis is performed. It is illustrated that the non-parametric method is a promising alternative to the more commonly applied MVAR-model based causality analysis. It is also demonstrated that, in the presence of both tested degradation factors, the DTF method is the most robust in terms of low estimation error, while the PDC in terms of low fictitious causal density. The dDTF provides lower fictitious causal density and higher spectral selectivity as compared to DTF, at high enough SNR. The GGC exhibits the worst compromise of performance. An application of the causality measures to a set of MEG resting-state experimental data is accordingly presented. It is demonstrated that significant contrast between the Eyes-Closed and Eyes-Open rest condition in the alpha frequency band allows to detect significant causality between the occipital cortex and the thalamus.

  2. Causal supports for early word learning.

    PubMed

    Booth, Amy E

    2009-01-01

    What factors determine whether a young child will learn a new word? Although there are surely numerous contributors, the current investigation highlights the role of causal information. Three-year-old children (N = 36) were taught 6 new words for unfamiliar objects or animals. Items were described in terms of their causal or noncausal properties. When tested only minutes after training, no significant differences between the conditions were evident. However, when tested several days after training, children performed better on words trained in the causal condition. These results demonstrate that the well-documented effect of causal information on learning and categorization extends to word learning in young children. PMID:19630905

  3. Assessing causality in multivariate accident models.

    PubMed

    Elvik, Rune

    2011-01-01

    This paper discusses the application of operational criteria of causality to multivariate statistical models developed to identify sources of systematic variation in accident counts, in particular the effects of variables representing safety treatments. Nine criteria of causality serving as the basis for the discussion have been developed. The criteria resemble criteria that have been widely used in epidemiology. To assess whether the coefficients estimated in a multivariate accident prediction model represent causal relationships or are non-causal statistical associations, all criteria of causality are relevant, but the most important criterion is how well a model controls for potentially confounding factors. Examples are given to show how the criteria of causality can be applied to multivariate accident prediction models in order to assess the relationships included in these models. It will often be the case that some of the relationships included in a model can reasonably be treated as causal, whereas for others such an interpretation is less supported. The criteria of causality are indicative only and cannot provide a basis for stringent logical proof of causality.

  4. Causal compensated perturbations in cosmology

    NASA Technical Reports Server (NTRS)

    Veeraraghavan, Shoba; Stebbins, Albert

    1990-01-01

    A theoretical framework is developed to calculate linear perturbations in the gravitational and matter fields which arise causally in response to the presence of stiff matter sources in a FRW cosmology. It is shown that, in order to satisfy energy and momentum conservation, the gravitational fields of the source must be compensated by perturbations in the matter and gravitational fields, and the role of such compensation in containing the initial inhomogeneities in their subsequent evolution is discussed. A complete formal solution is derived in terms of Green functions for the perturbations produced by an arbitrary source in a flat universe containing cold dark matter. Approximate Green function solutions are derived for the late-time density perturbations and late-time gravitational waves in a universe containing a radiation fluid. A cosmological energy-momentum pseudotensor is defined to clarify the nature of energy and momentum conservation in the expanding universe.

  5. Individual differences in causal uncertainty.

    PubMed

    Weary, G; Edwards, J A

    1994-08-01

    This article presents a scale that measures chronic individual differences in people's uncertainty about their ability to understand and detect cause-and-effect relationships in the social world: the Causal Uncertainty Scale (CUS). The results of Study 1 indicated that the scale has good internal and adequate test-retest reliability. Additionally, the results of a factor analysis suggested that the scale appears to be tapping a single construct. Study 2 examined the convergent and discriminant validity of the scale, and Studies 3 and 4 examined the predictive and incremental validity of the scale. The importance of the CUS to work on depressives' social information processing and for basic research and theory on human social judgment processes is discussed.

  6. 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…

  7. 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

  8. Causality and stability of cosmic jets

    NASA Astrophysics Data System (ADS)

    Porth, Oliver; Komissarov, Serguei S.

    2015-09-01

    In stark contrast to their laboratory and terrestrial counterparts, cosmic jets appear to be very stable. They are able to penetrate vast spaces, which exceed by up to a billion times the size of their central engines. We propose that the reason behind this remarkable property is the loss of causal connectivity across these jets, caused by their rapid expansion in response to fast decline of external pressure with the distance from the `jet engine'. In atmospheres with power-law pressure distribution, pext ∝ z-κ, the total loss of causal connectivity occurs, when κ > 2 - the steepness which is expected to be quite common for many astrophysical environments. This conclusion does not seem to depend on the physical nature of jets - it applies both to relativistic and non-relativistic flows, both magnetically dominated and unmagnetized jets. In order to verify it, we have carried out numerical simulations of moderately magnetized and moderately relativistic jets. The results give strong support to our hypothesis and provide with valuable insights. In particular, we find that the z-pinched inner cores of magnetic jets expand slower than their envelopes and become susceptible to instabilities even when the whole jet is stable. This may result in local dissipation and emission without global disintegration of the flow. Cosmic jets may become globally unstable when they enter flat sections of external atmospheres. We propose that the Fanaroff-Riley (FR) morphological division of extragalactic radio sources into two classes is related to this issue. In particular, we argue that the low power FR-I jets become reconfined, causally connected and globally unstable on the scale of galactic X-ray coronas, whereas more powerful FR-II jets reconfine much further out, already on the scale of radio lobes and remain largely intact until they terminate at hotspots. Using this idea, we derived the relationship between the critical jet power and the optical luminosity of the host

  9. 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%.

  10. 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

  11. Inference and Action in Early Causal Reasoning.

    ERIC Educational Resources Information Center

    Frye, Douglas; And Others

    1996-01-01

    Two marble-and-ramp experiments investigated whether a simple-to-embedded-rules account can explain changes in children's causal reasoning. Results indicated that the same difference between three- and four-year olds in the prediction experiment appeared in the action experiment, suggesting that the same rules may underlie causal action as well as…

  12. 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…

  13. 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…

  14. Campbell's and Rubin's Perspectives on Causal Inference

    ERIC Educational Resources Information Center

    West, Stephen G.; Thoemmes, Felix

    2010-01-01

    Donald Campbell's approach to causal inference (D. T. Campbell, 1957; W. R. Shadish, T. D. Cook, & D. T. Campbell, 2002) is widely used in psychology and education, whereas Donald Rubin's causal model (P. W. Holland, 1986; D. B. Rubin, 1974, 2005) is widely used in economics, statistics, medicine, and public health. Campbell's approach focuses on…

  15. 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.

  16. Causal Indicators Can Help to Interpret Factors

    ERIC Educational Resources Information Center

    Bentler, Peter M.

    2016-01-01

    The latent factor in a causal indicator model is no more than the latent factor of the factor part of the model. However, if the causal indicator variables are well-understood and help to improve the prediction of individuals' factor scores, they can help to interpret the meaning of the latent factor. Aguirre-Urreta, Rönkkö, and Marakas (2016)…

  17. 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…

  18. Causal Indicator Models: Identification, Estimation, and Testing

    ERIC Educational Resources Information Center

    Bollen, Kenneth A.; Davis, Walter R.

    2009-01-01

    We discuss the identification, estimation, and testing of structural equation models that have causal indicators. We first provide 2 rules of identification that are particularly helpful in models with causal indicators--the 2C emitted paths rule and the exogenous X rule. We demonstrate how these rules can help us distinguish identified from…

  19. 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…

  20. 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…

  1. Causal Moderation Analysis Using Propensity Score Methods

    ERIC Educational Resources Information Center

    Dong, Nianbo

    2012-01-01

    This paper is based on previous studies in applying propensity score methods to study multiple treatment variables to examine the causal moderator effect. The propensity score methods will be demonstrated in a case study to examine the causal moderator effect, where the moderators are categorical and continuous variables. Moderation analysis is an…

  2. A copula approach to assessing Granger causality.

    PubMed

    Hu, Meng; Liang, Hualou

    2014-10-15

    In neuroscience, as in many other fields of science and engineering, it is crucial to assess the causal interactions among multivariate time series. Granger causality has been increasingly used to identify causal influence between time series based on multivariate autoregressive models. Such an approach is based on linear regression framework with implicit Gaussian assumption of model noise residuals having constant variance. As a consequence, this measure cannot detect the cause-effect relationship in high-order moments and nonlinear causality. Here, we propose an effective model-free, copula-based Granger causality measure that can be used to reveal nonlinear and high-order moment causality. We first formulate Granger causality as the log-likelihood ratio in terms of conditional distribution, and then derive an efficient estimation procedure using conditional copula. We use resampling techniques to build a baseline null-hypothesis distribution from which statistical significance can be derived. We perform a series of simulations to investigate the performance of our copula-based Granger causality, and compare its performance against other state-of-the-art techniques. Our method is finally applied to neural field potential time series recorded from visual cortex of a monkey while performing a visual illusion task.

  3. Controlling for causally relevant third variables.

    PubMed

    Goodie, Adam S; Williams, Cristina C; Crooks, C L

    2003-10-01

    In 3 experiments, the authors tested the conditions under which 3rd variables are controlled for in making causal judgments. The authors hypothesized that 3rd variables are controlled for when the 3rd variables are themselves perceived as causal. In Experiment 1, the participants predicted test performance after seeing information about wearing a lucky garment, taking a test-preparation course, and staying up late. The course (perceived as more causally relevant) was controlled for more than was the garment (perceived as less causally relevant) in assessing the effectiveness of staying up late. In Experiments 2 and 3, to obviate the many alternative accounts that arise from the realistic cover story of Experiment 1, participants predicted flowers' blooming after the presentation or nonpresentation of liquids. When one liquid was trained as causal, it was controlled for more in judging another liquid than when it was trained as neutral. Overall, stimuli perceived as causal were controlled for more when judging other stimuli. The authors concluded that the effect of perceived causal relevance on causal conditionalizing is real and normatively reasonable. PMID:14672103

  4. 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…

  5. 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…

  6. Learning about causes from people: observational causal learning in 24-month-old infants.

    PubMed

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

    2012-09-01

    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.

  7. Recursive partitioning for heterogeneous causal effects

    PubMed Central

    Athey, Susan; Imbens, Guido

    2016-01-01

    In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies and for conducting hypothesis tests about the magnitude of differences in treatment effects across subsets of the population. We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects. The approach enables the construction of valid confidence intervals for treatment effects, even with many covariates relative to the sample size, and without “sparsity” assumptions. We propose an “honest” approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation. Our approach builds on regression tree methods, modified to optimize for goodness of fit in treatment effects and to account for honest estimation. Our model selection criterion anticipates that bias will be eliminated by honest estimation and also accounts for the effect of making additional splits on the variance of treatment effect estimates within each subpopulation. We address the challenge that the “ground truth” for a causal effect is not observed for any individual unit, so that standard approaches to cross-validation must be modified. Through a simulation study, we show that for our preferred method honest estimation results in nominal coverage for 90% confidence intervals, whereas coverage ranges between 74% and 84% for nonhonest approaches. Honest estimation requires estimating the model with a smaller sample size; the cost in terms of mean squared error of treatment effects for our preferred method ranges between 7–22%. PMID:27382149

  8. Confounding effects of phase delays on causality estimation.

    PubMed

    Vakorin, Vasily A; Mišić, Bratislav; Krakovska, Olga; Bezgin, Gleb; McIntosh, Anthony R

    2013-01-01

    Linear and non-linear techniques for inferring causal relations between the brain signals representing the underlying neuronal systems have become a powerful tool to extract the connectivity patterns in the brain. Typically these tools employ the idea of Granger causality, which is ultimately based on the temporal precedence between the signals. At the same time, phase synchronization between coupled neural ensembles is considered a mechanism implemented in the brain to integrate relevant neuronal ensembles to perform a cognitive or perceptual task. Phase synchronization can be studied by analyzing the effects of phase-locking between the brain signals. However, we should expect that there is no one-to-one mapping between the observed phase lag and the time precedence as specified by physically interacting systems. Specifically, phase lag observed between two signals may interfere with inferring causal relations. This could be of critical importance for the coupled non-linear oscillating systems, with possible time delays in coupling, when classical linear cross-spectrum strategies for solving phase ambiguity are not efficient. To demonstrate this, we used a prototypical model of coupled non-linear systems, and compared three typical pipelines of inferring Granger causality, as established in the literature. Specifically, we compared the performance of the spectral and information-theoretic Granger pipelines as well as standard Granger causality in their relations to the observed phase differences for frequencies at which the signals become synchronized to each other. We found that an information-theoretic approach, which takes into account different time lags between the past of one signal and the future of another signal, was the most robust to phase effects.

  9. Canonical Granger causality between regions of interest.

    PubMed

    Ashrafulla, Syed; Haldar, Justin P; Joshi, Anand A; Leahy, Richard M

    2013-12-01

    Estimating and modeling functional connectivity in the brain is a challenging problem with potential applications in the understanding of brain organization and various neurological and neuropsychological conditions. An important objective in connectivity analysis is to determine the connections between regions of interest in the brain. However, traditional functional connectivity analyses have frequently focused on modeling interactions between time series recordings at individual sensors, voxels, or vertices despite the fact that a single region of interest will often include multiple such recordings. In this paper, we present a novel measure of interaction between regions of interest rather than individual signals. The proposed measure, termed canonical Granger causality, combines ideas from canonical correlation and Granger causality analysis to yield a measure that reflects directed causality between two regions of interest. In particular, canonical Granger causality uses optimized linear combinations of signals from each region of interest to enable accurate causality measurements from substantially less data compared to alternative multivariate methods that have previously been proposed for this scenario. The optimized linear combinations are obtained using a variation of a technique developed for optimization on the Stiefel manifold. We demonstrate the advantages of canonical Granger causality in comparison to alternative causality measures for a range of different simulated datasets. We also apply the proposed measure to local field potential data recorded in a macaque brain during a visuomotor task. Results demonstrate that canonical Granger causality can be used to identify causal relationships between striate and prestriate cortexes in cases where standard Granger causality is unable to identify statistically significant interactions.

  10. 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. PMID:25813899

  11. Causal systems categories: differences in novice and expert categorization of causal phenomena.

    PubMed

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

    2012-07-01

    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 feedback vs. causal chain) and in their content domain (e.g., economics vs. biology). Our hypothesis was that there would be a shift from domain-based sorting to causal sorting with increasing expertise in the relevant domains. This prediction was borne out: the novice groups sorted primarily by domain and the expert group sorted by causal category. These results suggest that science training facilitates insight about causal structures.

  12. Spread of entanglement and causality

    NASA Astrophysics Data System (ADS)

    Casini, Horacio; Liu, Hong; Mezei, Márk

    2016-07-01

    We investigate causality constraints on the time evolution of entanglement entropy after a global quench in relativistic theories. We first provide a general proof that the so-called tsunami velocity is bounded by the speed of light. We then generalize the free particle streaming model of [1] to general dimensions and to an arbitrary entanglement pattern of the initial state. In more than two spacetime dimensions the spread of entanglement in these models is highly sensitive to the initial entanglement pattern, but we are able to prove an upper bound on the normalized rate of growth of entanglement entropy, and hence the tsunami velocity. The bound is smaller than what one gets for quenches in holographic theories, which highlights the importance of interactions in the spread of entanglement in many-body systems. We propose an interacting model which we believe provides an upper bound on the spread of entanglement for interacting relativistic theories. In two spacetime dimensions with multiple intervals, this model and its variations are able to reproduce intricate results exhibited by holographic theories for a significant part of the parameter space. For higher dimensions, the model bounds the tsunami velocity at the speed of light. Finally, we construct a geometric model for entanglement propagation based on a tensor network construction for global quenches.

  13. 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.

  14. 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

  15. 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

  16. 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. PMID:26191014

  17. 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.

  18. 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

  19. 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.

  20. Perception of causality in schizophrenia spectrum disorder.

    PubMed

    Tschacher, Wolfgang; Kupper, Zeno

    2006-10-01

    Patients with schizophrenia spectrum disorders often maintain deviating views on cause-effect relationships, especially when positive and disorganization symptoms are manifest. Altered perceived causality is prominent in delusional ideation, in ideas of reference, and in the mentalizing ability (theory of mind [ToM]) of patients. Perceiving causal relationships may be understood either as higher order cognitive reasoning or as low-level information processing. In the present study, perception of causality was investigated as a low-level, preattentional capability similar to gestalt-like perceptual organization. Thirty-one patients (24 men and 7 women with mean age 27.7 years) and the same number of healthy control subjects matched to patients with respect to age and sex were tested. A visual paradigm was used in which 2 identical discs move, from opposite sides of a monitor, steadily toward and then past one another. Their coincidence generates an ambiguous, bistable percept (discs either "stream through" or "bounce off" one another). The bouncing perception, ie, perceived causality, is enhanced when auditory stimuli are presented at the time of coincidence. Psychopathology was measured using the Positive and Negative Syndrome Scale. It was found that positive symptoms were strongly associated with increased perceived causality and disorganization with attenuated perceived causality. Patients in general were not significantly different from controls, but symptom subgroups showed specifically altered perceived causality. Perceived causality as a basic preattentional process may contribute to higher order cognitive alterations and ToM deficiencies. It is suggested that cognitive remediation therapy should address both increased and reduced perception of causality. PMID:16896057

  1. Tom Ten Have's contributions to causal inference and biostatistics: review and future research directions.

    PubMed

    Small, Dylan S; Joffe, Marshall M; Lynch, Kevin G; Roy, Jason A; Russell Localio, A

    2014-09-10

    Tom Ten Have made many contributions to causal inference and biostatistics before his untimely death. This paper reviews Tom's contributions and discusses potential related future research directions. We focus on Tom's contributions to longitudinal/repeated measures categorical data analysis and particularly his contributions to causal inference. Tom's work on causal inference was primarily in the areas of estimating the effect of receiving treatment in randomized trials with nonadherence and mediation analysis. A related area to mediation analysis he was working on at the time of his death was posttreatment effect modification with applications to designing adaptive treatment strategies.

  2. 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.

  3. Causality and methodology. Notes on thanatochronological estimations.

    PubMed

    Boniolo, Giovanni; Libero, Mirella; Aprile, Anna

    2005-01-01

    The authors propose some methodological considerations on thanatochronological estimations. They first consider the problem of the definition of death, and then they deal with the issue of the estimations of death time, that is, with the Post-Mortem Interval (PMI). As regards the first question, they note that it does not concern only the definition of death, but also the choice of a particular kind of definition of 'definition'. With reference to the second question, the authors suggest a causal model showing that the presence of many causal chains must be taken into consideration. Finally they discuss what 'most convenient and reliable causal chain' means for a thanatochronologist.

  4. Reward-Guided Learning with and without Causal Attribution.

    PubMed

    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

    2016-04-01

    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

  5. Zika Virus Infection and Microcephaly: Evidence for a Causal Link

    PubMed Central

    Wang, Jin-Na; Ling, Feng

    2016-01-01

    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. PMID:27775637

  6. Reward-Guided Learning with and without Causal Attribution.

    PubMed

    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

    2016-04-01

    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.

  7. 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

  8. Risk and causality in newspaper reporting.

    PubMed

    Boholm, Max

    2009-11-01

    The study addresses the textual representation of risk and causality in news media reporting. The analytical framework combines two theoretical perspectives: media frame analysis and the philosophy of causality. Empirical data derive from selected newspaper articles on risks in the Göta älv river valley in southwest Sweden from 1994 to 2007. News media content was coded and analyzed with respect to causal explanations of risk issues. At the level of individual articles, this study finds that the media provide simple causal explanations of risks such as water pollution, landslides, and flooding. Furthermore, these explanations are constructed, or framed, in various ways, the same risk being attributed to different causes in different articles. However, the study demonstrates that a fairly complex picture of risks in the media emerges when extensive material is analyzed systematically.

  9. Synergy, redundancy and unnormalized Granger causality.

    PubMed

    Stramaglia, S; Angelini, L; Cortes, J M; Marinazzo, D

    2015-08-01

    We analyze by means of Granger causality the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. Whilst fully conditioned Granger causality is not affected by synergy, the pairwise analysis fails to put in evidence synergetic effects. We show that maximization of the total Granger causality to a given target, over all the possible partitions of the set of driving variables, puts in evidence redundant multiplets of variables influencing the target, provided that an unnormalized definition of Granger causality is adopted. Along the same lines we also introduce a pairwise index of synergy (w.r.t. to information flow to a third variable) which is zero when two independent sources additively influence a common target; thus, this definition differs from previous definitions of synergy.

  10. 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. PMID:26986337

  11. Quantum probability assignment limited by relativistic causality

    PubMed Central

    Han, Yeong Deok; Choi, Taeseung

    2016-01-01

    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. PMID:26971717

  12. 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.

  13. Singular clues to causality and their use in human causal judgment.

    PubMed

    White, Peter A

    2014-01-01

    It is argued that causal understanding originates in experiences of acting on objects. Such experiences have consistent features that can be used as clues to causal identification and judgment. These are singular clues, meaning that they can be detected in single instances. A catalog of 14 singular clues is proposed. The clues function as heuristics for generating causal judgments under uncertainty and are a pervasive source of bias in causal judgment. More sophisticated clues such as mechanism clues and repeated interventions are derived from the 14. Research on the use of empirical information and conditional probabilities to identify causes has used scenarios in which several of the clues are present, and the use of empirical association information for causal judgment depends on the presence of singular clues. It is the singular clues and their origin that are basic to causal understanding, not multiple instance clues such as empirical association, contingency, and conditional probabilities. PMID:23957568

  14. 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. PMID:25571742

  15. Causal Inference for Vaccine Effects on Infectiousness

    PubMed Central

    Halloran, M. Elizabeth; Hudgens, Michael G.

    2012-01-01

    If a vaccine does not protect individuals completely against infection, it could still reduce infectiousness of infected vaccinated individuals to others. Typically, vaccine efficacy for infectiousness is estimated based on contrasts between the transmission risk to susceptible individuals from infected vaccinated individuals compared with that from infected unvaccinated individuals. Such estimates are problematic, however, because they are subject to selection bias and do not have a causal interpretation. Here, we develop causal estimands for vaccine efficacy for infectiousness for four different scenarios of populations of transmission units of size two. These causal estimands incorporate both principal stratification, based on the joint potential infection outcomes under vaccine and control, and interference between individuals within transmission units. In the most general scenario, both individuals can be exposed to infection outside the transmission unit and both can be assigned either vaccine or control. The three other scenarios are special cases of the general scenario where only one individual is exposed outside the transmission unit or can be assigned vaccine. The causal estimands for vaccine efficacy for infectiousness are well defined only within certain principal strata and, in general, are identifiable only with strong unverifiable assumptions. Nonetheless, the observed data do provide some information, and we derive large sample bounds on the causal vaccine efficacy for infectiousness estimands. An example of the type of data observed in a study to estimate vaccine efficacy for infectiousness is analyzed in the causal inference framework we developed. PMID:22499732

  16. Causality violation, gravitational shockwaves and UV completion

    NASA Astrophysics Data System (ADS)

    Hollowood, Timothy J.; Shore, Graham M.

    2016-03-01

    The effective actions describing the low-energy dynamics of QFTs involving gravity generically exhibit causality violations. These may take the form of superluminal propagation or Shapiro time advances and allow the construction of "time machines", i.e. spacetimes admitting closed non-spacelike curves. Here, we discuss critically whether such causality violations may be used as a criterion to identify unphysical effective actions or whether, and how, causality problems may be resolved by embedding the action in a fundamental, UV complete QFT. We study in detail the case of photon scattering in an Aichelburg-Sexl gravitational shockwave background and calculate the phase shifts in QED for all energies, demonstrating their smooth interpolation from the causality-violating effective action values at low-energy to their manifestly causal high-energy limits. At low energies, these phase shifts may be interpreted as backwards-in-time coordinate jumps as the photon encounters the shock wavefront, and we illustrate how the resulting causality problems emerge and are resolved in a two-shockwave time machine scenario. The implications of our results for ultra-high (Planck) energy scattering, in which graviton exchange is modelled by the shockwave background, are highlighted.

  17. Physics Without Causality — Theory and Evidence

    NASA Astrophysics Data System (ADS)

    Shoup, Richard

    2006-10-01

    The principle of cause and effect is deeply rooted in human experience, so much so that it is routinely and tacitly assumed throughout science, even by scientists working in areas where time symmetry is theoretically ingrained, as it is in both classical and quantum physics. Experiments are said to cause their results, not the other way around. In this informal paper, we argue that this assumption should be replaced with a more general notion of mutual influence — bi-directional relations or constraints on joint values of two or more variables. From an analysis based on quantum entropy, it is proposed that quantum measurement is a unitary three-interaction, with no collapse, no fundamental randomness, and no barrier to backward influence. Experimental results suggesting retrocausality are seen frequently in well-controlled laboratory experiments in parapsychology and elsewhere, especially where a random element is included. Certain common characteristics of these experiments give the appearance of contradicting well-established physical laws, thus providing an opportunity for deeper understanding and important clues that must be addressed by any explanatory theory. We discuss how retrocausal effects and other anomalous phenomena can be explained without major injury to existing physical theory. A modified quantum formalism can give new insights into the nature of quantum measurement, randomness, entanglement, causality, and time.

  18. Causality-imposed (Kramers-Kronig) relationships between attenuation and dispersion.

    PubMed

    Waters, Kendall R; Mobley, Joel; Miller, James G

    2005-05-01

    Causality imposes restrictions on both the time-domain and frequency-domain responses of a system. The Kramers-Kronig (K-K) relations relate the real and imaginary parts of the frequency-domain response. In ultrasonics, K-K relations often are used to link attenuation and dispersion. We review both integral and differential forms of the frequency-domain K-K relations that are relevant to theoretical models and laboratory measurements. We consider two methods for implementing integral K-K relations for the case of finite-bandwidth data, namely, extrapolation of data and restriction of integration limits. For the latter approach, we discuss the accuracy of K-K predictions for specific classes of system behavior and how the truncation of the integrals affects this accuracy. We demonstrate the accurate prediction of attenuation and dispersion using several forms of the K-K relations relevant to experimental measurements of media with attenuation coefficients obeying a frequency power law and media consisting of resonant scatterers. We also review the time-causal relations that describe the time-domain consequences of causality in the wave equation. These relations can be thought of as time-domain analogs of the (frequency-domain) K-K relations. Causality-imposed relations, such as the K-K and time-causal relations, provide useful tools for the analysis of measurements and models of acoustic systems.

  19. Causal evolutions of bulk local excitations from CFT

    NASA Astrophysics Data System (ADS)

    Goto, Kanato; Miyaji, Masamichi; Takayanagi, Tadashi

    2016-09-01

    Bulk localized excited states in an AdS spacetime can be constructed from Ishibashi states with respect to the global conformal symmetry in the dual CFT. We study boundary two point functions of primary operators in the presence of bulk localized excitations in two dimensional CFTs. From two point functions in holographic CFTs, we observe causal propagations of radiations when the mass of dual bulk scalar field is close to the BF bound. This behavior for holographic CFTs is consistent with the locality and causality in classical gravity duals. We also show that this cannot be seen in free fermion CFTs. Moreover, we find that the short distance behavior of two point functions is universal and obeys the relation which generalizes the first law of entanglement entropy.

  20. Research designs and making causal inferences from health care studies.

    PubMed

    Flannelly, Kevin J; Jankowski, Katherine R B

    2014-01-01

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

  1. Who caused it? Interpersonal causal inferences in young children.

    PubMed

    Vikan, A; Skevik, H

    1992-01-01

    Three experiments were designed to test 4- and 6-year-old children's causal inferences in interpersonal settings where emotions (glad, angry, and sad) were effect responses. The results showed that emotion and orientation (towards or away from) were central cues, and that sex and age also were used to some extent. Cues related to regularity philosophic notions (e.g. David Hume), such as contiguity in time and space, and time order of cause and effect were little used by comparison. The results raise questions about the basic role attributed to regularity cues both by philosophers and psychologists, and suggest a multiple cue contribution rather than a basic cue generalization approach to causal cognition development.

  2. Quantifying information transfer and mediation along causal pathways in complex systems

    NASA Astrophysics Data System (ADS)

    Runge, Jakob

    2015-12-01

    Measures of information transfer have become a popular approach to analyze interactions in complex systems such as the Earth or the human brain from measured time series. Recent work has focused on causal definitions of information transfer aimed at decompositions of predictive information about a target variable, while excluding effects of common drivers and indirect influences. While common drivers clearly constitute a spurious causality, the aim of the present article is to develop measures quantifying different notions of the strength of information transfer along indirect causal paths, based on first reconstructing the multivariate causal network. Another class of novel measures quantifies to what extent different intermediate processes on causal paths contribute to an interaction mechanism to determine pathways of causal information transfer. The proposed framework complements predictive decomposition schemes by focusing more on the interaction mechanism between multiple processes. A rigorous mathematical framework allows for a clear information-theoretic interpretation that can also be related to the underlying dynamics as proven for certain classes of processes. Generally, however, estimates of information transfer remain hard to interpret for nonlinearly intertwined complex systems. But if experiments or mathematical models are not available, then measuring pathways of information transfer within the causal dependency structure allows at least for an abstraction of the dynamics. The measures are illustrated on a climatological example to disentangle pathways of atmospheric flow over Europe.

  3. The influence of virtual sample size on confidence and causal-strength judgments.

    PubMed

    Liljeholm, Mimi; Cheng, Patricia W

    2009-01-01

    The authors investigated whether confidence in causal judgments varies with virtual sample size--the frequency of cases in which the outcome is (a) absent before the introduction of a generative cause or (b) present before the introduction of a preventive cause. Participants were asked to evaluate the influence of various candidate causes on an outcome as well as to rate their confidence in those judgments. They were presented with information on the relative frequencies of the outcome given the presence and absence of various candidate causes. These relative frequencies, sample size, and the direction of the causal influence (generative vs. preventive) were manipulated. It was found that both virtual and actual sample size affected confidence. Further, confidence affected estimates of strength, but confidence and strength are dissociable. The results enable a consistent explanation of the puzzling previous finding that observed causal-strength ratings often deviated from the predictions of both of the 2 dominant models of causal strength.

  4. Income inequality and population health: correlation and causality.

    PubMed

    Babones, Salvatore J

    2008-04-01

    A large literature now exists on the cross-national correlation between income inequality and population health, but existing studies suffer from sparse data, poor operationalization of income inequality, and the use of low-power statistical models. This paper sets out to estimate the ecological correlation between income inequality and indicators of population health in a very broad panel of countries, to demonstrate that this relationship is largely non-artifactual, and to test whether this relationship might be causal. Gini coefficients of national income inequality in 1970 and 1995 are correlated with life expectancy, infant mortality rates, and murder rates, controlling for national income per capita. In cross-sectional analyses, inequality is significantly correlated with life expectancy, infant mortality, and (inconsistently) the murder rate. The health correlations are shown to be not primarily due to the "convexity effect" of the non-linear relationship between individual income and individual health, which seems to account for no more than one-third of the relationship between inequality and health, and likely much less. Change in inequality 1970-1995 is significantly related to change in life expectancy and infant mortality, suggesting a causal relationship, but these correlations are not robust with respect to sample or controls. It can be concluded that there is a strong, consistent, statistically significant, non-artifactual correlation between national income inequality and population health, but though there is some evidence that this relationship is causal, the relative stability of income inequality over time in most countries makes causality difficult to test.

  5. Singular Clues to Causality and Their Use in Human Causal Judgment

    ERIC Educational Resources Information Center

    White, Peter A.

    2014-01-01

    It is argued that causal understanding originates in experiences of acting on objects. Such experiences have consistent features that can be used as clues to causal identification and judgment. These are singular clues, meaning that they can be detected in single instances. A catalog of 14 singular clues is proposed. The clues function as…

  6. The Cradle of Causal Reasoning: Newborns' Preference for Physical Causality

    ERIC Educational Resources Information Center

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

    2013-01-01

    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…

  7. The Mental Representation of Causal Conditional Reasoning: Mental Models or Causal Models

    ERIC Educational Resources Information Center

    Ali, Nilufa; Chater, Nick; Oaksford, Mike

    2011-01-01

    In this paper, two experiments are reported investigating the nature of the cognitive representations underlying causal conditional reasoning performance. The predictions of causal and logical interpretations of the conditional diverge sharply when inferences involving "pairs" of conditionals--such as "if P[subscript 1] then Q" and "if P[subscript…

  8. Causal impressions: predicting when, not just whether.

    PubMed

    Young, Michael E; Rogers, Ester T; Beckmann, Joshua S

    2005-03-01

    In 1739, David Hume established the so-called cues to causality--environmental cues that are important to the inference of causality. Although this descriptive account has been corroborated experimentally, it has not been established why these cues are useful, except that they may reflect statistical regularities in the environment. One of the cues to causality, covariation, helps predict whether an effect will occur, but not its time of occurrence. In the present study, evidence is provided that spatial and temporal contiguity improve an observer's ability to predict when an effect will occur, thus complementing the utility of covariation as a predictor of whether an effect will occur. While observing Michotte's (1946/1963) launching effect, participants showed greater accuracy and precision in their predictions of the onset of movement by the launched object when there was spatial and temporal contiguity. Furthermore, when auditory cues that bridged a delayed launch were included, causal ratings and predictability were similarly affected. These results suggest that the everyday inference of causality relies on our ability to predict whether and when an effect will occur.

  9. Normalizing the causality between time series

    NASA Astrophysics Data System (ADS)

    Liang, X. San

    2015-08-01

    Recently, a rigorous yet concise formula was derived to evaluate information flow, and hence the causality in a quantitative sense, between time series. To assess the importance of a resulting causality, it needs to be normalized. The normalization is achieved through distinguishing a Lyapunov exponent-like, one-dimensional phase-space stretching rate and a noise-to-signal ratio from the rate of information flow in the balance of the marginal entropy evolution of the flow recipient. It is verified with autoregressive models and applied to a real financial analysis problem. An unusually strong one-way causality is identified from IBM (International Business Machines Corporation) to GE (General Electric Company) 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 mainframe computer market.

  10. The causal meaning of Hamilton's rule.

    PubMed

    Okasha, Samir; Martens, Johannes

    2016-03-01

    Hamilton's original derivation of his rule for the spread of an altruistic gene (rb>c) assumed additivity of costs and benefits. Recently, it has been argued that an exact version of the rule holds under non-additive pay-offs, so long as the cost and benefit terms are suitably defined, as partial regression coefficients. However, critics have questioned both the biological significance and the causal meaning of the resulting rule. This paper examines the causal meaning of the generalized Hamilton's rule in a simple model, by computing the effect of a hypothetical experiment to assess the cost of a social action and comparing it to the partial regression definition. The two do not agree. A possible way of salvaging the causal meaning of Hamilton's rule is explored, by appeal to R. A. Fisher's 'average effect of a gene substitution'.

  11. The causal meaning of Hamilton's rule.

    PubMed

    Okasha, Samir; Martens, Johannes

    2016-03-01

    Hamilton's original derivation of his rule for the spread of an altruistic gene (rb>c) assumed additivity of costs and benefits. Recently, it has been argued that an exact version of the rule holds under non-additive pay-offs, so long as the cost and benefit terms are suitably defined, as partial regression coefficients. However, critics have questioned both the biological significance and the causal meaning of the resulting rule. This paper examines the causal meaning of the generalized Hamilton's rule in a simple model, by computing the effect of a hypothetical experiment to assess the cost of a social action and comparing it to the partial regression definition. The two do not agree. A possible way of salvaging the causal meaning of Hamilton's rule is explored, by appeal to R. A. Fisher's 'average effect of a gene substitution'. PMID:27069669

  12. On the Inference of Functional Circadian Networks Using Granger Causality

    PubMed Central

    Pourzanjani, Arya; Herzog, Erik D.; Petzold, Linda R.

    2015-01-01

    Being able to infer one way direct connections in an oscillatory network such as the suprachiastmatic nucleus (SCN) of the mammalian brain using time series data is difficult but crucial to understanding network dynamics. Although techniques have been developed for inferring networks from time series data, there have been no attempts to adapt these techniques to infer directional connections in oscillatory time series, while accurately distinguishing between direct and indirect connections. In this paper an adaptation of Granger Causality is proposed that allows for inference of circadian networks and oscillatory networks in general called Adaptive Frequency Granger Causality (AFGC). Additionally, an extension of this method is proposed to infer networks with large numbers of cells called LASSO AFGC. The method was validated using simulated data from several different networks. For the smaller networks the method was able to identify all one way direct connections without identifying connections that were not present. For larger networks of up to twenty cells the method shows excellent performance in identifying true and false connections; this is quantified by an area-under-the-curve (AUC) 96.88%. We note that this method like other Granger Causality-based methods, is based on the detection of high frequency signals propagating between cell traces. Thus it requires a relatively high sampling rate and a network that can propagate high frequency signals. PMID:26413748

  13. Causality, renormalizability and ultra-high energy gravitational scattering

    NASA Astrophysics Data System (ADS)

    Hollowood, Timothy J.; Shore, Graham M.

    2016-05-01

    The amplitude { A }(s,t) for ultra-high energy scattering can be found in the leading eikonal approximation by considering propagation in an Aichelburg–Sexl gravitational shockwave background. Loop corrections in the QFT describing the scattered particles are encoded for energies below the Planck scale in an effective action which in general exhibits causality violation and Shapiro time advances. In this paper, we use Penrose limit techniques to calculate the full energy dependence of the scattering phase shift {{{\\Theta }}}{{scat}}(\\hat{s}), where the single variable \\hat{s}={Gs}/{m}2{b}d-2 contains both the CM energy s and impact parameter b, for a range of scalar QFTs in d dimensions with different renormalizability properties. We evaluate the high-energy limit of {{{\\Theta }}}{{scat}}(\\hat{s}) and show in detail how causality is related to the existence of a well-defined UV completion. Similarities with graviton scattering and the corresponding resolution of causality violation in the effective action by string theory are briefly discussed.

  14. Causal patch complementarity: The inside story for old black holes

    NASA Astrophysics Data System (ADS)

    Ilgin, Irfan; Yang, I.-Sheng

    2014-02-01

    We carefully analyze the causal patches which belong to observers falling into an old black hole. We show that without a distillation-like process, the Almheiri-Marolf-Polchinski-Sully (AMPS) paradox cannot challenge complementarity. That is because the two ingredients for the paradox, the interior region and the early Hawking radiation, cannot be spacelike separated and both low energy within any single causal patch. Either the early quanta have Planckian wavelengths, or the interior region is exponentially smaller than the Schwarzschild size. This means that their appearances in the low-energy theory are strictly timelike separated, which nullifies the problem of double entanglement/purity or quantum cloning. This verifies that the AMPS paradox is either only a paradox in the global description like the original information paradox, or a direct consequence of the assumption that a distillation process is feasible without hidden consequences. We discuss possible relations to cosmological causal patches and the possibility of transferring energy without transferring quantum information.

  15. Causal models of trip replanning in TravTek

    SciTech Connect

    Schryver, J.C.

    1998-07-01

    The TravTek operational field test was conducted to evaluate the effectiveness of route planning, route guidance and various navigational aiding modalities for Advanced Traveler Information Systems in ground vehicles. A causal network was constructed in order to achieve a better understanding of the dependencies among variables implicated in the replanning process. Causal inferences were modeled using path analysis techniques. The original Yoked Driver study reported that addition of real-time navigation planning did not increase trip efficiency during initial trip planning. Data mining of the relatively complete database revealed that the incidence of dynamic trip replanning was only 0.51% or 1 out of every 198 trips. Nevertheless, the replanning acceptance rate was 92.8%, suggesting that less conservative criteria might have been acceptable to drivers. Several points can be made based upon the path analysis techniques. Drivers who rejected better route offers were more likely to be male renters; rejected routes were apparently offered at earlier times with a lower predicted time savings and fewer maneuvers. Failure to accept a better route also apparently resulted in fewer wrong-turn deviations. Contrary to expectations, wrong-turn count and time loss appeared as semi-independent hubs in the resultant causal network. Implications of the path analysis are discussed. Proposals for in-vehicle information systems are formulated to increase driver participation as co-planner, and increase the likelihood that trip replanning will positively impact trip efficiency.

  16. Implications of Einstein-Weyl Causality on Quantum Mechanics

    NASA Astrophysics Data System (ADS)

    Bendaniel, David

    A fundamental physical principle that has consequences for the topology of space-time is the principle of Einstein-Weyl causality. This also has quantum mechanical manifestations. Borchers and Sen have rigorously investigated the mathematical implications of Einstein-Weyl causality and shown the denumerable space-time Q2 would be implied. They were left with important philosophical paradoxes regarding the nature of the physical real line E, e.g., whether E = R, the real line of mathematics. In order to remove these paradoxes an investigation into a constructible foundation is suggested. We have pursued such a program and find it indeed provides a dense, denumerable space-time and, moreover, an interesting connection with quantum mechanics. We first show that this constructible theory contains polynomial functions which are locally homeomorphic with a dense, denumerable metric space R* and are inherently quantized. Eigenfunctions governing fields can then be effectively obtained by computational iteration. Postulating a Lagrangian for fields in a compactified space-time, we get a general description of which the Schrodinger equation is a special case. From these results we can then also show that this denumerable space-time is relational (in the sense that space is not infinitesimally small if and only if it contains a quantized field) and, since Q2 is imbedded in R*2, it directly fulfills the strict topological requirements for Einstein-Weyl causality. Therefore, the theory predicts that E = R*.

  17. On the Inference of Functional Circadian Networks Using Granger Causality.

    PubMed

    Pourzanjani, Arya; Herzog, Erik D; Petzold, Linda R

    2015-01-01

    Being able to infer one way direct connections in an oscillatory network such as the suprachiastmatic nucleus (SCN) of the mammalian brain using time series data is difficult but crucial to understanding network dynamics. Although techniques have been developed for inferring networks from time series data, there have been no attempts to adapt these techniques to infer directional connections in oscillatory time series, while accurately distinguishing between direct and indirect connections. In this paper an adaptation of Granger Causality is proposed that allows for inference of circadian networks and oscillatory networks in general called Adaptive Frequency Granger Causality (AFGC). Additionally, an extension of this method is proposed to infer networks with large numbers of cells called LASSO AFGC. The method was validated using simulated data from several different networks. For the smaller networks the method was able to identify all one way direct connections without identifying connections that were not present. For larger networks of up to twenty cells the method shows excellent performance in identifying true and false connections; this is quantified by an area-under-the-curve (AUC) 96.88%. We note that this method like other Granger Causality-based methods, is based on the detection of high frequency signals propagating between cell traces. Thus it requires a relatively high sampling rate and a network that can propagate high frequency signals.

  18. A significant causal association between C-reactive protein levels and schizophrenia

    PubMed Central

    Inoshita, Masatoshi; Numata, Shusuke; Tajima, Atsushi; Kinoshita, Makoto; Umehara, Hidehiro; Nakataki, Masahito; Ikeda, Masashi; Maruyama, Souichiro; Yamamori, Hidenaga; Kanazawa, Tetsufumi; Shimodera, Shinji; Hashimoto, Ryota; Imoto, Issei; Yoneda, Hiroshi; Iwata, Nakao; Ohmori, Tetsuro

    2016-01-01

    Many observational studies have shown elevated blood CRP levels in schizophrenia compared with controls, and one population-based prospective study has reported that elevated plasma CRP levels were associated with late- and very-late-onset schizophrenia. Furthermore, several clinical studies have reported the efficacy of anti-inflammatory drugs on the symptoms in patients with schizophrenia. However, whether elevated CRP levels are causally related to schizophrenia is not still established because of confounding factors and reverse causality. In the present study, we demonstrated that serum CRP levels were significantly higher in patients with schizophrenia than in the controls by conducting a case-control study and a meta-analysis of case-control studies between schizophrenia and serum CRP levels. Furthermore, we provided evidence for a causal association between elevated CRP levels and increased schizophrenia risk by conducting a Mendelian randomization analysis. Our findings suggest that elevated CRP itself may be a causal risk factor for schizophrenia. PMID:27193331

  19. Building a bridge—an archeologist's perspective on the evolution of causal cognition

    PubMed Central

    Haidle, Miriam N.

    2014-01-01

    The cognitive capacities of fossil humans cannot be studied directly. Taking the evolution of causal cognition as an example this article demonstrates the use of bridging arguments from archeological finds as starting point via identification/classification, behavioral reconstructions, and cognitive interpretations to psychological models. Generally, tool use is linked to some causal understanding/agent construal as the tool broadens the subject's specific capabilities by adding new characters to its action sphere. In human evolution, the distance between the primarily perceived problem and the solution satisfying this need increased markedly: from simple causal relations to effective chaining in secondary/modular tool use, and further to the use of composite tools, complementary tool sets and notional tools. This article describes the evolution of human tool behavior from the perspective of problem-solution-distance and discusses the implications for a linked development of causal cognition. PMID:25566147

  20. Detecting causality from nonlinear dynamics with short-term time series.

    PubMed

    Ma, Huanfei; Aihara, Kazuyuki; Chen, Luonan

    2014-12-12

    Quantifying causality between variables from observed time series data is of great importance in various disciplines but also a challenging task, especially when the observed data are short. Unlike the conventional methods, we find it possible to detect causality only with very short time series data, based on embedding theory of an attractor for nonlinear dynamics. Specifically, we first show that measuring the smoothness of a cross map between two observed variables can be used to detect a causal relation. Then, we provide a very effective algorithm to computationally evaluate the smoothness of the cross map, or "Cross Map Smoothness" (CMS), and thus to infer the causality, which can achieve high accuracy even with very short time series data. Analysis of both mathematical models from various benchmarks and real data from biological systems validates our method.

  1. Analysis of complex causal networks through time series

    NASA Astrophysics Data System (ADS)

    Hut, R.; van de Giesen, N.

    2008-12-01

    We introduce a new way of looking at (the relations between) groups of signals. In complex networks, such as in landscapes and ecosystems, multiple factors influence each other either through direct causal relations or indirectly through intermediate variables. To puzzle apart the causal relations in a complex network on the basis of measured time series, is not trivial. The method developed here allows us to do excalty that. Using relations that can be derived by (classical) multiple input multiple output system identification, we construct underlying networks of linear time-invariant systems that describe the direct relations between the different signals. The structure of this underlying network can provide valuable information about which signals are dominant, which relations between signals are dominant, and which signals affect each other through another signal in stead of directly. Feedback is easily identified using this approach. We show that the Eigenvalues of the underlying network determine the stability of the network as a whole. Applications are foreseen in for instance the fields of data-driven climate modeling as well as other research involving time series analysis in complex networks.

  2. 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.

  3. Pride and Prejudice and Causal Indicators

    ERIC Educational Resources Information Center

    Lee, Nick; Chamberlain, Laura

    2016-01-01

    Aguirre-Urreta, Rönkkö, and Marakas' (2016) paper in "Measurement: Interdisciplinary Research and Perspectives" (hereafter referred to as ARM2016) is an important and timely piece of scholarship, in that it provides strong analytic support to the growing theoretical literature that questions the underlying ideas behind causal and…

  4. Comments: Causal Interpretations of Mediation Effects

    ERIC Educational Resources Information Center

    Jo, Booil; Stuart, Elizabeth A.

    2012-01-01

    The authors thank Dr. Lindsay Page for providing a nice illustration of the use of the principal stratification framework to define causal effects, and a Bayesian model for effect estimation. They hope that her well-written article will help expose education researchers to these concepts and methods, and move the field of mediation analysis in…

  5. Escaping Myopia: Teaching Students about Historical Causality

    ERIC Educational Resources Information Center

    Waring, Scott M.

    2010-01-01

    There are so many aspects to teaching history that are vital to creating well-rounded historical thinkers, but one of the most fundamental and most overlooked elements is the idea of causality. Far too many students do not understand the idea of causation, that there are multiple reasons for why historical events occurred and transpired in the way…

  6. Causality and Teleology in High School Biology.

    ERIC Educational Resources Information Center

    Tamir, Pinchas

    1985-01-01

    Ability to distinguish between causal (cause-effect) and teleological (means-ends) explanations was measured in 1905 twelfth-grade biology students and found to be dependent on student knowledge. Although the inability to make these distinctions contributes to misconceptions in biology, appropriate instruction can easily remedy the problem. Sample…

  7. Inductive Reasoning about Causally Transmitted Properties

    ERIC Educational Resources Information Center

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

    2008-01-01

    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'…

  8. Marriage and Anomie: A Causal Argument

    ERIC Educational Resources Information Center

    Lee, Gary R.

    1974-01-01

    A sample of 394 married couples is employed to test the possibility of an association between marital satisfaction and personal (attitudinal) anomie. The hypothesis is supported. Conclusions are offered relevant to anomie theory, and to utilization of marital and family phenomena as independent variables in causal explanations of nonfamily events.…

  9. Causal Measurement Models: Can Criticism Stimulate Clarification?

    ERIC Educational Resources Information Center

    Markus, Keith A.

    2016-01-01

    In their 2016 work, Aguirre-Urreta et al. provided a contribution to the literature on causal measurement models that enhances clarity and stimulates further thinking. Aguirre-Urreta et al. presented a form of statistical identity involving mapping onto the portion of the parameter space involving the nomological net, relationships between the…

  10. THE CHILD'S CONCEPTION OF PHYSICAL CAUSALITY.

    ERIC Educational Resources Information Center

    PIAGET, JEAN

    THE CHILD'S CONCEPTION OF PHYSICAL CAUSALITY WAS INVESTIGATED. THREE METHODS OF INVESTIGATION WERE USED. THE FIRST METHOD WAS PURELY VERBAL, AND CONSISTED OF A SERIES OF QUESTIONS DIRECTED TO CHILDREN, REGARDING SOME NATURAL PHENOMENON. THE SECOND METHOD INVOLVED A HALF-VERBAL, HALF-PRACTICAL APPROACH, WHEREIN A SPECIFIC REFERENCE TO NATURAL…

  11. Constructing Causal Diagrams to Learn Deliberation

    ERIC Educational Resources Information Center

    Easterday, Matthew W.; Aleven, Vincent; Scheines, Richard; Carver, Sharon M.

    2009-01-01

    Policy problems like "What should we do about global warming?" are ill-defined in large part because we do not agree on a system to represent them the way we agree Algebra problems should be represented by equations. As a first step toward building a policy deliberation tutor, we investigated: (a) whether causal diagrams help students learn to…

  12. Strong curvature singularities and causal simplicity

    SciTech Connect

    Krolak, A. )

    1992-02-01

    Techniques of differential topology in Lorentzian manifolds developed by Geroch, Hawking, and Penrose are used to rule out a class of locally naked strong curvature singularities in strongly causal space-times. This result yields some support to the validity of Penrose's strong cosmic censorship hypothesis.

  13. Linear Response Laws and Causality in Electrodynamics

    ERIC Educational Resources Information Center

    Yuffa, Alex J.; Scales, John A.

    2012-01-01

    Linear response laws and causality (the effect cannot precede the cause) are of fundamental importance in physics. In the context of classical electrodynamics, students often have a difficult time grasping these concepts because the physics is obscured by the intermingling of the time and frequency domains. In this paper, we analyse the linear…

  14. Time and Order Effects on Causal Learning

    ERIC Educational Resources Information Center

    Alvarado, Angelica; Jara, Elvia; Vila, Javier; Rosas, Juan M.

    2006-01-01

    Five experiments were conducted to explore trial order and retention interval effects upon causal predictive judgments. Experiment 1 found that participants show a strong effect of trial order when a stimulus was sequentially paired with two different outcomes compared to a condition where both outcomes were presented intermixed. Experiment 2…

  15. The metagenomic approach and causality in virology.

    PubMed

    Castrignano, Silvana Beres; Nagasse-Sugahara, Teresa Keico

    2015-01-01

    Nowadays, the metagenomic approach has been a very important tool in the discovery of new viruses in environmental and biological samples. Here we discuss how these discoveries may help to elucidate the etiology of diseases and the criteria necessary to establish a causal association between a virus and a disease.

  16. Introducing Mechanics by Tapping Core Causal Knowledge

    ERIC Educational Resources Information Center

    Klaassen, Kees; Westra, Axel; Emmett, Katrina; Eijkelhof, Harrie; Lijnse, Piet

    2008-01-01

    This article concerns an outline of an introductory mechanics course. It is based on the argument that various uses of the concept of force (e.g. from Kepler, Newton and everyday life) share an explanatory strategy based on core causal knowledge. The strategy consists of (a) the idea that a force causes a deviation from how an object would move of…

  17. Causality and the Levels of Selection.

    PubMed

    Krupp, D B

    2016-04-01

    When is it sensible to say that group selection has shaped organismal design? This question has prompted many replies but few credible solutions. New work that exposes the causal relationships between phenotypes and fitness may finally settle the matter - and a few other things besides.

  18. The metagenomic approach and causality in virology

    PubMed Central

    Castrignano, Silvana Beres; Nagasse-Sugahara, Teresa Keico

    2015-01-01

    Nowadays, the metagenomic approach has been a very important tool in the discovery of new viruses in environmental and biological samples. Here we discuss how these discoveries may help to elucidate the etiology of diseases and the criteria necessary to establish a causal association between a virus and a disease. PMID:25902566

  19. Causal and Teleological Explanations in Biology

    ERIC Educational Resources Information Center

    Yip, Cheng-Wai

    2009-01-01

    A causal explanation in biology focuses on the mechanism by which a biological process is brought about, whereas a teleological explanation considers the end result, in the context of the survival of the organism, as a reason for certain biological processes or structures. There is a tendency among students to offer a teleological explanation…

  20. Identification of Dominant Excitation Patterns and Sources of Atrial Fibrillation by Causality Analysis.

    PubMed

    Rodrigo, Miguel; Climent, Andreu M; Liberos, Alejandro; Calvo, David; Fernández-Avilés, Francisco; Berenfeld, Omer; Atienza, Felipe; Guillem, Maria S

    2016-08-01

    Burden of atrial fibrillation (AF) can be reduced by ablation of sources of electrical impulses driving AF but driver identification is still challenging. This study presents a new methodology based on causality analysis that allows identifying the hierarchically dominant areas driving AF. Identification of dominant propagation patterns was achieved by computing causal relations between intracardiac multi-electrode catheter recordings of four paroxysmal AF patients during sinus rhythm, pacing and AF. In addition, realistic mathematical models of the atria during AF were used to validate the methodology both in the presence and absence of dominant frequency (DF) gradients. During electrical pacing, sources of propagation patterns detected by causality analysis were consistent with the location of the stimulating catheter. During AF, propagation patterns presented temporal variability, but a dominant direction accounted for significantly more propagations than other directions (49 ± 15% vs. 14 ± 13% or less, p < 0.01). Both in patients with a DF gradient and in mathematical models, causal maps allowed the identification of sites responsible for maintenance of AF. Causal maps allowed the identification of atrial dominant sites. In particular, causality analysis resulted in stable dominant cause-effect propagation directions during AF and could serve as a guide for performing ablation procedures in AF patients. PMID:26850022

  1. Identification of Dominant Excitation Patterns and Sources of Atrial Fibrillation by Causality Analysis.

    PubMed

    Rodrigo, Miguel; Climent, Andreu M; Liberos, Alejandro; Calvo, David; Fernández-Avilés, Francisco; Berenfeld, Omer; Atienza, Felipe; Guillem, Maria S

    2016-08-01

    Burden of atrial fibrillation (AF) can be reduced by ablation of sources of electrical impulses driving AF but driver identification is still challenging. This study presents a new methodology based on causality analysis that allows identifying the hierarchically dominant areas driving AF. Identification of dominant propagation patterns was achieved by computing causal relations between intracardiac multi-electrode catheter recordings of four paroxysmal AF patients during sinus rhythm, pacing and AF. In addition, realistic mathematical models of the atria during AF were used to validate the methodology both in the presence and absence of dominant frequency (DF) gradients. During electrical pacing, sources of propagation patterns detected by causality analysis were consistent with the location of the stimulating catheter. During AF, propagation patterns presented temporal variability, but a dominant direction accounted for significantly more propagations than other directions (49 ± 15% vs. 14 ± 13% or less, p < 0.01). Both in patients with a DF gradient and in mathematical models, causal maps allowed the identification of sites responsible for maintenance of AF. Causal maps allowed the identification of atrial dominant sites. In particular, causality analysis resulted in stable dominant cause-effect propagation directions during AF and could serve as a guide for performing ablation procedures in AF patients.

  2. Causal simulation and sensor planning in predictive monitoring

    NASA Technical Reports Server (NTRS)

    Doyle, Richard J.

    1989-01-01

    Two issues are addressed which arise in the task of detecting anomalous behavior in complex systems with numerous sensor channels: how to adjust alarm thresholds dynamically, within the changing operating context of the system, and how to utilize sensors selectively, so that nominal operation can be verified reliably without processing a prohibitive amount of sensor data. The approach involves simulation of a causal model of the system, which provides information on expected sensor values, and on dependencies between predicted events, useful in assessing the relative importance of events so that sensor resources can be allocated effectively. The potential applicability of this work to the execution monitoring of robot task plans is briefly discussed.

  3. Implications of the causality principle for ultra chiral metamaterials

    PubMed Central

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

    2015-01-01

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

  4. 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

  5. The influence of causal connections between symptoms on the diagnosis of mental disorders: evidence from online and offline measures.

    PubMed

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

    2014-09-01

    An experiment conducted with students and experienced clinicians demonstrated very fast and online causal reasoning in the diagnosis of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) mental disorders. The experiment also demonstrated that clinicians' causal reasoning is triggered by information that is directly related to the causal structure that explains the symptoms, such as their temporal sequence. The use of causal theories was measured through explicit, verbal diagnostic judgments and through the online registration of participants' reading times of clinical reports. To detect both online and offline causal reasoning, the consistency of clinical reports was manipulated. This manipulation was made by varying the temporal order in which different symptoms developed in hypothetical clients, and by providing explicit information about causal connections between symptoms. The temporal order of symptoms affected the clinicians' but not the students' reading times. However, offline diagnostic judgments in both groups were influenced by the consistency manipulation. Overall, our results suggest that clinicians engage in fast and online causal reasoning processes when dealing with diagnostic information concerning mental disorders, and that both clinicians and students engage in causal reasoning in diagnostic judgment tasks. PMID:25068482

  6. Vitamin D and extra-skeletal health: causality or consequence.

    PubMed

    Al Nozha, Omar M

    2016-07-01

    Vitamin D deficiency /insufficiency is widely recognized as a global health problem that is likely to be involved in pathogenesis or progression of many acute and chronic health disorders. Its relation to skeletal health has been clearly demonstrated and thoroughly examined. This review aims to highlight the continuous debate about the relation between vitamin D and extra-skeletal health and whether it is a causality or just an association. Overall, the available evidence does not meet the criteria for establishing cause-and-effect relationships because of the limitations of observational studies to corroborate the causality due to many potential confounders. Moreover, the causal relationship couldn't be established in randomized studies or in many meta-analyses. This may reflect the fact that vitamin D level reduction is just a biomarker of ill health. The inflammatory processes involved in the disease occurrence and the functional limitations of the diseases would have a role in reducing serum 25-hydroxy vitamin D "25 (OH) D" level, which would explain why low vitamin D is reported in a wide range of disorders. This may underscore the possibility of harm instead of benefit of vitamin D supplementation when its exact role is not fully established, thus many guidelines and interest groups are still hesitant toward recommending replacement in extra-skeletal disease. Future directions entails the need for a large well-designed randomized control trials (RCTs) to resolve the active debate on the benefits of vitamin D replacement for extra-skeletal disease, and not only that, future studies should establish specific, clinically relevant effects of vitamin D repletion, provide cut-values for optimal serum levels of 25 (OH) D, and appropriate doses for non-skeletal health benefits.

  7. Vitamin D and extra-skeletal health: causality or consequence

    PubMed Central

    Al Nozha, Omar M.

    2016-01-01

    Vitamin D deficiency /insufficiency is widely recognized as a global health problem that is likely to be involved in pathogenesis or progression of many acute and chronic health disorders. Its relation to skeletal health has been clearly demonstrated and thoroughly examined. This review aims to highlight the continuous debate about the relation between vitamin D and extra-skeletal health and whether it is a causality or just an association. Overall, the available evidence does not meet the criteria for establishing cause-and-effect relationships because of the limitations of observational studies to corroborate the causality due to many potential confounders. Moreover, the causal relationship couldn’t be established in randomized studies or in many meta-analyses. This may reflect the fact that vitamin D level reduction is just a biomarker of ill health. The inflammatory processes involved in the disease occurrence and the functional limitations of the diseases would have a role in reducing serum 25-hydroxy vitamin D “25 (OH) D” level, which would explain why low vitamin D is reported in a wide range of disorders. This may underscore the possibility of harm instead of benefit of vitamin D supplementation when its exact role is not fully established, thus many guidelines and interest groups are still hesitant toward recommending replacement in extra-skeletal disease. Future directions entails the need for a large well-designed randomized control trials (RCTs) to resolve the active debate on the benefits of vitamin D replacement for extra-skeletal disease, and not only that, future studies should establish specific, clinically relevant effects of vitamin D repletion, provide cut-values for optimal serum levels of 25 (OH) D, and appropriate doses for non-skeletal health benefits. PMID:27610068

  8. Vitamin D and extra-skeletal health: causality or consequence.

    PubMed

    Al Nozha, Omar M

    2016-07-01

    Vitamin D deficiency /insufficiency is widely recognized as a global health problem that is likely to be involved in pathogenesis or progression of many acute and chronic health disorders. Its relation to skeletal health has been clearly demonstrated and thoroughly examined. This review aims to highlight the continuous debate about the relation between vitamin D and extra-skeletal health and whether it is a causality or just an association. Overall, the available evidence does not meet the criteria for establishing cause-and-effect relationships because of the limitations of observational studies to corroborate the causality due to many potential confounders. Moreover, the causal relationship couldn't be established in randomized studies or in many meta-analyses. This may reflect the fact that vitamin D level reduction is just a biomarker of ill health. The inflammatory processes involved in the disease occurrence and the functional limitations of the diseases would have a role in reducing serum 25-hydroxy vitamin D "25 (OH) D" level, which would explain why low vitamin D is reported in a wide range of disorders. This may underscore the possibility of harm instead of benefit of vitamin D supplementation when its exact role is not fully established, thus many guidelines and interest groups are still hesitant toward recommending replacement in extra-skeletal disease. Future directions entails the need for a large well-designed randomized control trials (RCTs) to resolve the active debate on the benefits of vitamin D replacement for extra-skeletal disease, and not only that, future studies should establish specific, clinically relevant effects of vitamin D repletion, provide cut-values for optimal serum levels of 25 (OH) D, and appropriate doses for non-skeletal health benefits. PMID:27610068

  9. Dyslipidemias in the prevention of cardiovascular disease: risks and causality.

    PubMed

    Graham, Ian; Cooney, Marie-Therese; Bradley, David; Dudina, Alexandra; Reiner, Zeljko

    2012-12-01

    Atherosclerotic cardiovascular disease is now the major global cause of death, despite reductions in CVD deaths in developed societies. Dyslipidemias are a major contributor, but the mass occurrence of CVD relates to the combined effects of hyperlipidemia, hypertension, and smoking. Total blood cholesterol and LDL-cholesterol relate to CVD risk in an independent and graded manner and fulfill the criteria for causality. Therapeutic reduction of these lipid fractions is associated with improved outcomes. There is good evidence that HDL-cholesterol, triglycerides, and Lp(a) relate to CVD although the evidence for a causal relationship is weaker. The HDL association with CVD is largely independent of other risk factors whereas triglycerides may be more important as signaling a need to look intensively for other measures of risk such as central obesity, hypertension, low HDL-cholesterol, and glucose intolerance. Lp(a) is an inherited risk marker. The benefit of lowering it is uncertain, but it may be that its impact on risk is attenuated if LDL-cholesterol is low.

  10. 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…

  11. CAUSAL ANALYSIS AND PROBABILITY DATA: EXAMPLES FOR IMPAIRED AQUATIC CONDITION

    EPA Science Inventory

    Causal analysis is plausible reasoning applied to diagnosing observed effect(s), for example, diagnosing

    cause of biological impairment in a stream. Sir Bradford Hill basically defined the application of causal

    analysis when he enumerated the elements of causality f...

  12. Rationales in Children's Causal Learning from Others' Actions

    ERIC Educational Resources Information Center

    Sobel, David M.; Sommerville, Jessica A.

    2009-01-01

    Shown commensurate actions and information by an adult, preschoolers' causal learning was influenced by the pedagogical context in which these actions occurred. Four-year-olds who were provided with a reason for an experimenter's action relevant to learning causal structure showed more accurate causal learning than children exposed to the same…

  13. A Quantitative Causal Model Theory of Conditional Reasoning

    ERIC Educational Resources Information Center

    Fernbach, Philip M.; Erb, Christopher D.

    2013-01-01

    The authors propose and test a causal model theory of reasoning about conditional arguments with causal content. According to the theory, the acceptability of modus ponens (MP) and affirming the consequent (AC) reflect the conditional likelihood of causes and effects based on a probabilistic causal model of the scenario being judged. Acceptability…

  14. 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…

  15. A Bayesian Theory of Sequential Causal Learning and Abstract Transfer

    ERIC Educational Resources Information Center

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

    2016-01-01

    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…

  16. Perceived Causalities of Physical Events Are Influenced by Social Cues

    ERIC Educational Resources Information Center

    Zhou, Jifan; Huang, Xiang; Jin, Xinyi; Liang, Junying; Shui, Rende; Shen, Mowei

    2012-01-01

    In simple mechanical events, we can directly perceive causal interactions of the physical objects. Physical cues (especially spatiotemporal features of the display) are found to associate with causal perception. Here, we demonstrate that cues of a completely different domain--"social cues"--also impact the causal perception of "physical" events:…

  17. Assessing Understanding of Complex Causal Networks Using an Interactive Game

    ERIC Educational Resources Information Center

    Ross, Joel

    2013-01-01

    Assessing people's understanding of the causal relationships found in large-scale complex systems may be necessary for addressing many critical social concerns, such as environmental sustainability. Existing methods for assessing systems thinking and causal understanding frequently use the technique of cognitive causal mapping. However, the…

  18. A Self-Agency Bias in Preschoolers' Causal Inferences

    ERIC Educational Resources Information Center

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

    2009-01-01

    Preschoolers' causal learning from intentional actions--causal interventions--is subject to a self-agency bias. The authors propose that this bias is evidence-based, in other words, that it is responsive to causal uncertainty. In the current studies, two causes (one child controlled, one experimenter controlled) were associated with one or two…

  19. Omission of Causal Indicators: Consequences and Implications for Measurement

    ERIC Educational Resources Information Center

    Aguirre-Urreta, Miguel I.; Rönkkö, Mikko; Marakas, George M.

    2016-01-01

    One of the central assumptions of the causal-indicator literature is that all causal indicators must be included in the research model and that the exclusion of one or more relevant causal indicators would have severe negative consequences by altering the meaning of the latent variable. In this research we show that the omission of a relevant…

  20. Toward an Intersectional Understanding of Process Causality and Social Context

    ERIC Educational Resources Information Center

    Anderson, Gary L.; Scott, Janelle

    2012-01-01

    Maxwell and Donmoyer both argue in this issue of "Qualitative Inquiry" that narrow definitions of causality in educational research tend to disqualify qualitative research from influence (and funding) among policy makers. They propose a process view of causality that would allow qualitative researchers to make causal claims more grounded in the…

  1. Recursive retrospective revaluation of causal judgments.

    PubMed

    Macho, Siegfried; Burkart, Judith

    2002-11-01

    Recursive causal evaluation is an iterative process in which the evaluation of a target cause, T, is based on the outcome of the evaluation of another cause, C, the evaluation of which itself depends on the evaluation of a 3rd cause, D. Retrospective revaluation consists of backward processing of information as indicated by the fact that the evaluation of T is influenced by subsequent information that is not concerned with T directly. Two experiments demonstrate recursive retrospective revaluation with contingency information presented in list format as well as with trial-by-trial acquisition. Existing associative models are unable to predict the results. The model of recursive causal disambiguation that conceptualizes the revaluation as a recursive process of disambiguation predicts the pattern of results correctly.

  2. Localizing epileptic seizure onsets with Granger causality

    NASA Astrophysics Data System (ADS)

    Adhikari, Bhim M.; Epstein, Charles M.; Dhamala, Mukesh

    2013-09-01

    Accurate localization of the epileptic seizure onset zones (SOZs) is crucial for successful surgery, which usually depends on the information obtained from intracranial electroencephalography (IEEG) recordings. The visual criteria and univariate methods of analyzing IEEG recordings have not always produced clarity on the SOZs for resection and ultimate seizure freedom for patients. Here, to contribute to improving the localization of the SOZs and to understanding the mechanism of seizure propagation over the brain, we applied spectral interdependency methods to IEEG time series recorded from patients during seizures. We found that the high-frequency (>80 Hz) Granger causality (GC) occurs before the onset of any visible ictal activity and causal relationships involve the recording electrodes where clinically identifiable seizures later develop. These results suggest that high-frequency oscillatory network activities precede and underlie epileptic seizures, and that GC spectral measures derived from IEEG can assist in precise delineation of seizure onset times and SOZs.

  3. Causal Network Inference Via Group Sparse Regularization.

    PubMed

    Bolstad, Andrew; Van Veen, Barry D; Nowak, Robert

    2011-06-11

    This paper addresses the problem of inferring sparse causal networks modeled by multivariate autoregressive (MAR) processes. Conditions are derived under which the Group Lasso (gLasso) procedure consistently estimates sparse network structure. The key condition involves a "false connection score" ψ. In particular, we show that consistent recovery is possible even when the number of observations of the network is far less than the number of parameters describing the network, provided that ψ < 1. The false connection score is also demonstrated to be a useful metric of recovery in nonasymptotic regimes. The conditions suggest a modified gLasso procedure which tends to improve the false connection score and reduce the chances of reversing the direction of causal influence. Computational experiments and a real network based electrocorticogram (ECoG) simulation study demonstrate the effectiveness of the approach.

  4. Blocking and unblocking in human causal learning.

    PubMed

    Le Pelley, M E; Oakeshott, S M; McLaren, I P L

    2005-01-01

    Three experiments sought to develop the suggestion that, under some circumstances, common associative learning mechanisms might underlie animal conditioning and human causal learning, by demonstrating, in humans, an effect analogous to the unblocking by reinforcer omission observed in animal conditioning. Experiment 1 found no such effect. Experiment 2, designed to prevent inhibitory influences that might have masked excitatory unblocking in Experiment 1, demonstrated unblocking, indicating common human-animal associative learning mechanisms in which the associability of a stimulus varies as a function of its predictive history. Experiment 3, using a similar design but with a procedure promoting application of rational inference processes, failed to detect the same unblocking effect, indicating that associative and cognitive mechanisms may influence human causal learning.

  5. Causal Network Inference Via Group Sparse Regularization

    PubMed Central

    Bolstad, Andrew; Van Veen, Barry D.; Nowak, Robert

    2011-01-01

    This paper addresses the problem of inferring sparse causal networks modeled by multivariate autoregressive (MAR) processes. Conditions are derived under which the Group Lasso (gLasso) procedure consistently estimates sparse network structure. The key condition involves a “false connection score” ψ. In particular, we show that consistent recovery is possible even when the number of observations of the network is far less than the number of parameters describing the network, provided that ψ < 1. The false connection score is also demonstrated to be a useful metric of recovery in nonasymptotic regimes. The conditions suggest a modified gLasso procedure which tends to improve the false connection score and reduce the chances of reversing the direction of causal influence. Computational experiments and a real network based electrocorticogram (ECoG) simulation study demonstrate the effectiveness of the approach. PMID:21918591

  6. Causality Is Inconsistent With Quantum Field Theory

    SciTech Connect

    Wolf, Fred Alan

    2011-11-29

    Causality in quantum field theory means the vanishing of commutators for spacelike separated fields (VCSSF). I will show that VCSSF is not tenable. For VCSSF to be tenable, and therefore, to have both retarded and advanced propagators vanish in the elsewhere, a superposition of negative energy antiparticle and positive energy particle propagators, traveling forward in time, and a superposition of negative energy particle and positive energy antiparticle propagators, traveling backward in time, are required. Hence VCSSF predicts non-vanishing probabilities for both negative energy particles in the forward-through-time direction and positive energy antiparticles in the backwards-through-time direction. Therefore, since VCSSF is unrealizable in a stable universe, tachyonic propagation must occur in denial of causality.

  7. The Causal Effects of Father Absence

    PubMed Central

    McLanahan, Sara; Tach, Laura; Schneider, Daniel

    2014-01-01

    The literature on father absence is frequently criticized for its use of cross-sectional data and methods that fail to take account of possible omitted variable bias and reverse causality. We review studies that have responded to this critique by employing a variety of innovative research designs to identify the causal effect of father absence, including studies using lagged dependent variable models, growth curve models, individual fixed effects models, sibling fixed effects models, natural experiments, and propensity score matching models. Our assessment is that studies using more rigorous designs continue to find negative effects of father absence on offspring well-being, although the magnitude of these effects is smaller than what is found using traditional cross-sectional designs. The evidence is strongest and most consistent for outcomes such as high school graduation, children’s social-emotional adjustment, and adult mental health. PMID:24489431

  8. Loop anomalies in the causal approach

    NASA Astrophysics Data System (ADS)

    Grigore, Dan-Radu

    2015-01-01

    We consider gauge models in the causal approach and study one-loop contributions to the chronological products and the anomalies they produce. We prove that in order greater than 4 there are no one-loop anomalies. Next we analyze one-loop anomalies in the second- and third-order of the perturbation theory. We prove that the even parity contributions (with respect to parity) do not produce anomalies; for the odd parity contributions we reobtain the well-known result.

  9. Symmetry and causality properties of physical fields

    PubMed Central

    Jakobsen, H. P.; Ørsted, B.; Segal, I. E.; Speh, B.; Vergne, M.

    1978-01-01

    Representations of groups of causality-preserving transformations on locally Minkowskian space-times, by actions on classes of wave functions of designated transformation properties, are analyzed, in extension of the conventional theoretical treatment of free relativistic particles. In particular, the constraints of positivity of the energy and finiteness of propagation velocity are developed, and the concept of mass is explored, within the indicated framework. PMID:16592512

  10. Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms

    PubMed Central

    Aalen, OO; Røysland, K; Gran, JM; Kouyos, R

    2014-01-01

    Directed acyclic graphs (DAGs) play a large role in the modern approach to causal inference. DAGs describe the relationship between measurements taken at various discrete times including the effect of interventions. The causal mechanisms, on the other hand, would naturally be assumed to be a continuous process operating over time in a cause–effect fashion. How does such immediate causation, that is causation occurring over very short time intervals, relate to DAGs constructed from discrete observations? We introduce a time-continuous model and simulate discrete observations in order to judge the relationship between the DAG and the immediate causal model. We find that there is no clear relationship; indeed the Bayesian network described by the DAG may not relate to the causal model. Typically, discrete observations of a process will obscure the conditional dependencies that are represented in the underlying mechanistic model of the process. It is therefore doubtful whether DAGs are always suited to describe causal relationships unless time is explicitly considered in the model. We relate the issues to mechanistic modeling by using the concept of local (in)dependence. An example using data from the Swiss HIV Cohort Study is presented. PMID:24463886

  11. Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples

    PubMed Central

    Hirata, Yoshito; Amigó, José M.; Matsuzaka, Yoshiya; Yokota, Ryo; Mushiake, Hajime; Aihara, Kazuyuki

    2016-01-01

    Identifying causal relations from time series is the first step to understanding the behavior of complex systems. Although many methods have been proposed, few papers have applied multiple methods together to detect causal relations based on time series generated from coupled nonlinear systems with some unobserved parts. Here we propose the combined use of three methods and a majority vote to infer causality under such circumstances. Two of these methods are proposed here for the first time, and all of the three methods can be applied even if the underlying dynamics is nonlinear and there are hidden common causes. We test our methods with coupled logistic maps, coupled Rössler models, and coupled Lorenz models. In addition, we show from ice core data how the causal relations among the temperature, the CH4 level, and the CO2 level in the atmosphere changed in the last 800,000 years, a conclusion also supported by irregularly sampled data analysis. Moreover, these methods show how three regions of the brain interact with each other during the visually cued, two-choice arm reaching task. Especially, we demonstrate that this is due to bottom up influences at the beginning of the task, while there exist mutual influences between the posterior medial prefrontal cortex and the presupplementary motor area. Based on our results, we conclude that identifying causality with an appropriate ensemble of multiple methods ensures the validity of the obtained results more firmly. PMID:27380515

  12. Reliability of the Granger causality inference

    NASA Astrophysics Data System (ADS)

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

    2014-04-01

    How to characterize information flows in physical, biological, and social systems remains a major theoretical challenge. Granger causality (GC) analysis has been widely used to investigate information flow through causal interactions. We address one of the central questions in GC analysis, that is, the reliability of the GC evaluation and its implications for the causal structures extracted by this analysis. Our work reveals that the manner in which a continuous dynamical process is projected or coarse-grained to a discrete process has a profound impact on the reliability of the GC inference, and different sampling may potentially yield completely opposite inferences. This inference hazard is present for both linear and nonlinear processes. We emphasize that there is a hazard of reaching incorrect conclusions about network topologies, even including statistical (such as small-world or scale-free) properties of the networks, when GC analysis is blindly applied to infer the network topology. We demonstrate this using a small-world network for which a drastic loss of small-world attributes occurs in the reconstructed network using the standard GC approach. We further show how to resolve the paradox that the GC analysis seemingly becomes less reliable when more information is incorporated using finer and finer sampling. Finally, we present strategies to overcome these inference artifacts in order to obtain a reliable GC result.

  13. Causal tapestries for psychology and physics.

    PubMed

    Sulis, William H

    2012-04-01

    Archetypal dynamics is a formal approach to the modeling of information flow in complex systems used to study emergence. It is grounded in the Fundamental Triad of realisation (system), interpretation (archetype) and representation (formal model). Tapestries play a fundamental role in the framework of archetypal dynamics as a formal representational system. They represent information flow by means of multi layered, recursive, interlinked graphical structures that express both geometry (form or sign) and logic (semantics). This paper presents a detailed mathematical description of a specific tapestry model, the causal tapestry, selected for use in describing behaving systems such as appear in psychology and physics from the standpoint of Process Theory. Causal tapestries express an explicit Lorentz invariant transient now generated by means of a reality game. Observables are represented by tapestry informons while subjective or hidden components (for example intellectual and emotional processes) are incorporated into the reality game that determines the tapestry dynamics. As a specific example, we formulate a random graphical dynamical system using causal tapestries.

  14. Dynamic causal modeling with genetic algorithms.

    PubMed

    Pyka, M; Heider, D; Hauke, S; Kircher, T; Jansen, A

    2011-01-15

    In the last years, dynamic causal modeling has gained increased popularity in the neuroimaging community as an approach for the estimation of effective connectivity from functional magnetic resonance imaging (fMRI) data. The algorithm calls for an a priori defined model, whose parameter estimates are subsequently computed upon the given data. As the number of possible models increases exponentially with additional areas, it rapidly becomes inefficient to compute parameter estimates for all models in order to reveal the family of models with the highest posterior probability. In the present study, we developed a genetic algorithm for dynamic causal models and investigated whether this evolutionary approach can accelerate the model search. In this context, the configuration of the intrinsic, extrinsic and bilinear connection matrices represents the genetic code and Bayesian model selection serves as a fitness function. Using crossover and mutation, populations of models are created and compared with each other. The most probable ones survive the current generation and serve as a source for the next generation of models. Tests with artificially created data sets show that the genetic algorithm approximates the most plausible models faster than a random-driven brute-force search. The fitness landscape revealed by the genetic algorithm indicates that dynamic causal modeling has excellent properties for evolution-driven optimization techniques.

  15. Bell's theorem on arbitrary causal structures

    NASA Astrophysics Data System (ADS)

    Fritz, Tobias

    2014-03-01

    Bell's theorem is a gedankenexperiment with an underlying causal structure in the form of the letter ``M.'' I will describe how such a Bell scenario is a special case of a vastly larger class of scenarios, in which the causal structure of the ``M'' is replaced by an arbitrary directed acyclic graph (or, equivalently, by a causal set). In this formalism, the apparent difference between the notions of ``choice of setting,'' ``source,'' and ``measurement'' disappears completely and all of these become special cases of the general notion of ``event.'' I will explain how this relieves Bell's theorem of the philosophical baggage associated with free will and also present several mathematical results about these more general scenarios obtained by various people. This formalism is expected to have applications in many other areas of science: it is relevant whenever a system is probed at certain points in space and time, and at each of these points there may be hidden information not observed by the probes.

  16. Causal tapestries for psychology and physics.

    PubMed

    Sulis, William H

    2012-04-01

    Archetypal dynamics is a formal approach to the modeling of information flow in complex systems used to study emergence. It is grounded in the Fundamental Triad of realisation (system), interpretation (archetype) and representation (formal model). Tapestries play a fundamental role in the framework of archetypal dynamics as a formal representational system. They represent information flow by means of multi layered, recursive, interlinked graphical structures that express both geometry (form or sign) and logic (semantics). This paper presents a detailed mathematical description of a specific tapestry model, the causal tapestry, selected for use in describing behaving systems such as appear in psychology and physics from the standpoint of Process Theory. Causal tapestries express an explicit Lorentz invariant transient now generated by means of a reality game. Observables are represented by tapestry informons while subjective or hidden components (for example intellectual and emotional processes) are incorporated into the reality game that determines the tapestry dynamics. As a specific example, we formulate a random graphical dynamical system using causal tapestries. PMID:22452929

  17. Causality, initial conditions, and inflationary magnetogenesis

    NASA Astrophysics Data System (ADS)

    Tsagas, Christos G.

    2016-05-01

    The post-inflationary evolution of inflation-produced magnetic fields, conventional or not, can change dramatically when two fundamental issues are accounted for. The first is causality, which demands that local physical processes can never affect superhorizon perturbations. The second is the nature of the transition from inflation to reheating and then to the radiation era, which determine the initial conditions at the start of these epochs. Causality implies that inflationary magnetic fields do not freeze into the matter until they have re-entered the causal horizon. The nature of the cosmological transitions and the associated initial conditions, on the other hand, determine the large-scale magnetic evolution after inflation. Put together, the two can slow down the adiabatic magnetic decay on superhorizon scales throughout the Universe's post-inflationary evolution and thus lead to considerably stronger residual magnetic fields. This is "good news" for both the conventional and the nonconventional scenarios of cosmic magnetogenesis. Mechanisms operating outside standard electromagnetism, in particular, do not need to enhance their fields too much during inflation in order to produce seeds that can feed the galactic dynamo today. In fact, even conventionally produced inflationary magnetic fields might be able to sustain the dynamo.

  18. Geometry of the infalling causal patch

    NASA Astrophysics Data System (ADS)

    Freivogel, Ben; Jefferson, Robert A.; Kabir, Laurens; Yang, I.-Sheng

    2015-02-01

    The firewall paradox states that an observer falling into an old black hole must see a violation of unitarity, locality, or the equivalence principle. Motivated by this remarkable conflict, we analyze the causal structure of black hole spacetimes in order to determine whether all the necessary ingredients for the paradox fit within a single observer's causal patch. We particularly focus on the question of whether the interior partner modes of the outgoing Hawking quanta can, in principle, be measured by an infalling observer. Since the relevant modes are spread over the entire sphere, we answer a simple geometrical question: can any observer see an entire sphere behind the horizon? We find that for all static black holes in 3 +1 and higher dimensions, with any value of the cosmological constant, no single observer can see both the early Hawking radiation and the interior modes with low angular momentum. We present a detailed description of the causal patch geometry of the Schwarzschild black hole in 3 +1 dimensions, where an infalling observer comes closest to being able to measure the relevant modes.

  19. A causal dispositional account of fitness.

    PubMed

    Triviño, Vanessa; Nuño de la Rosa, Laura

    2016-09-01

    The notion of fitness is usually equated to reproductive success. However, this actualist approach presents some difficulties, mainly the explanatory circularity problem, which have lead philosophers of biology to offer alternative definitions in which fitness and reproductive success are distinguished. In this paper, we argue  that none of these alternatives is satisfactory and, inspired by Mumford and Anjum's dispositional theory of causation, we offer a definition of fitness as a causal dispositional property. We argue that, under this framework, the distinctiveness that biologists usually attribute to fitness-namely, the fact that fitness is something different from both the physical traits of an organism and the number of offspring it leaves-can be explained, and the main problems associated with the concept of fitness can be solved. Firstly, we introduce Mumford and Anjum's dispositional theory of causation and present our definition of fitness as a causal disposition. We explain in detail each of the elements involved in our definition, namely: the relationship between fitness and the functional dispositions that compose it, the emergent character of fitness, and the context-sensitivity of fitness. Finally, we explain how fitness and realized fitness, as well as expected and realized fitness are distinguished in our approach to fitness as a causal disposition.

  20. Curvature constraints from the causal entropic principle

    SciTech Connect

    Bozek, Brandon; Albrecht, Andreas; Phillips, Daniel

    2009-07-15

    Current cosmological observations indicate a preference for a cosmological constant that is drastically smaller than what can be explained by conventional particle physics. The causal entropic principle (Bousso et al.) provides an alternative approach to anthropic attempts to predict our observed value of the cosmological constant by calculating the entropy created within a causal diamond. We have extended this work to use the causal entropic principle to predict the preferred curvature within the 'multiverse'. We have found that values larger than {rho}{sub k}=40{rho}{sub m} are disfavored by more than 99.99% peak value at {rho}{sub {lambda}}=7.9x10{sup -123} and {rho}{sub k}=4.3{rho}{sub m} for open universes. For universes that allow only positive curvature or both positive and negative curvature, we find a correlation between curvature and dark energy that leads to an extended region of preferred values. Our universe is found to be disfavored to an extent depending on the priors on curvature. We also provide a comparison to previous anthropic constraints on open universes and discuss future directions for this work.

  1. 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.

  2. 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,…

  3. The Role of Experience in Adult Thinking about Evidence for Causal Interpretations. 1984:02.

    ERIC Educational Resources Information Center

    Svensson, Lennart

    An interview investigation was undertaken in which a group of nurses and a group of technicians reasoned about evidence for the existence of a causal relation in a medical and in a technical case. A statement about the existence of the relation was presented and the subjects were then asked as to what evidence there might and should be behind such…

  4. Cognitive Neuroscience and Causal Inference: Implications for Psychiatry

    PubMed Central

    Dijkstra, Nadine; de Bruin, Leon

    2016-01-01

    In this paper, we investigate to what extent it is justified to draw conclusions about causal relations between brain states and mental states from cognitive neuroscience studies. We first explain the views of two prominent proponents of the interventionist account of causation: Woodward and Baumgartner. We then discuss the implications of their views in the context of traditional cognitive neuroscience studies in which the effect of changes in mental state on changes in brain states is investigated. After this, we turn to brain stimulation studies in which brain states are manipulated to investigate the effects on mental states. We argue that, depending on whether one sides with Woodward or Baumgartner, it is possible to draw causal conclusions from both types of studies (Woodward) or from brain stimulation studies only (Baumgartner). We show what happens to these conclusions if we adopt different views of the relation between mental states and brain states. Finally, we discuss the implications of our findings for psychiatry and the treatment of psychiatric disorders. PMID:27486408

  5. Cognitive Neuroscience and Causal Inference: Implications for Psychiatry.

    PubMed

    Dijkstra, Nadine; de Bruin, Leon

    2016-01-01

    In this paper, we investigate to what extent it is justified to draw conclusions about causal relations between brain states and mental states from cognitive neuroscience studies. We first explain the views of two prominent proponents of the interventionist account of causation: Woodward and Baumgartner. We then discuss the implications of their views in the context of traditional cognitive neuroscience studies in which the effect of changes in mental state on changes in brain states is investigated. After this, we turn to brain stimulation studies in which brain states are manipulated to investigate the effects on mental states. We argue that, depending on whether one sides with Woodward or Baumgartner, it is possible to draw causal conclusions from both types of studies (Woodward) or from brain stimulation studies only (Baumgartner). We show what happens to these conclusions if we adopt different views of the relation between mental states and brain states. Finally, we discuss the implications of our findings for psychiatry and the treatment of psychiatric disorders. PMID:27486408

  6. Neural Connectivity in Epilepsy as Measured by Granger Causality

    PubMed Central

    Coben, Robert; Mohammad-Rezazadeh, Iman

    2015-01-01

    Epilepsy is a chronic neurological disorder characterized by repeated seizures or excessive electrical discharges in a group of brain cells. Prevalence rates include about 50 million people worldwide and 10% of all people have at least one seizure at one time in their lives. Connectivity models of epilepsy serve to provide a deeper understanding of the processes that control and regulate seizure activity. These models have received initial support and have included measures of EEG, MEG, and MRI connectivity. Preliminary findings have shown regions of increased connectivity in the immediate regions surrounding the seizure foci and associated low connectivity in nearby regions and pathways. There is also early evidence to suggest that these patterns change during ictal events and that these changes may even by related to the occurrence or triggering of seizure events. We present data showing how Granger causality can be used with EEG data to measure connectivity across brain regions involved in ictal events and their resolution. We have provided two case examples as a demonstration of how to obtain and interpret such data. EEG data of ictal events are processed, converted to independent components and their dipole localizations, and these are used to measure causality and connectivity between these locations. Both examples have shown hypercoupling near the seizure foci and low causality across nearby and associated neuronal pathways. This technique also allows us to track how these measures change over time and during the ictal and post-ictal periods. Areas for further research into this technique, its application to epilepsy, and the formation of more effective therapeutic interventions are recommended. PMID:26236211

  7. Risk-Based Causal Modeling of Airborne Loss of Separation

    NASA Technical Reports Server (NTRS)

    Geuther, Steven C.; Shih, Ann T.

    2015-01-01

    Maintaining safe separation between aircraft remains one of the key aviation challenges as the Next Generation Air Transportation System (NextGen) emerges. The goals of the NextGen are to increase capacity and reduce flight delays to meet the aviation demand growth through the 2025 time frame while maintaining safety and efficiency. The envisioned NextGen is expected to enable high air traffic density, diverse fleet operations in the airspace, and a decrease in separation distance. All of these factors contribute to the potential for Loss of Separation (LOS) between aircraft. LOS is a precursor to a potential mid-air collision (MAC). The NASA Airspace Operations and Safety Program (AOSP) is committed to developing aircraft separation assurance concepts and technologies to mitigate LOS instances, therefore, preventing MAC. This paper focuses on the analysis of causal and contributing factors of LOS accidents and incidents leading to MAC occurrences. Mid-air collisions among large commercial aircraft are rare in the past decade, therefore, the LOS instances in this study are for general aviation using visual flight rules in the years 2000-2010. The study includes the investigation of causal paths leading to LOS, and the development of the Airborne Loss of Separation Analysis Model (ALOSAM) using Bayesian Belief Networks (BBN) to capture the multi-dependent relations of causal factors. The ALOSAM is currently a qualitative model, although further development could lead to a quantitative model. ALOSAM could then be used to perform impact analysis of concepts and technologies in the AOSP portfolio on the reduction of LOS risk.

  8. 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.

  9. Causal connectivity of evolved neural networks during behavior.

    PubMed

    Seth, Anil K

    2005-03-01

    To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics. PMID:16350433

  10. How contrast situations affect the assignment of causality in symmetric physical settings

    PubMed Central

    Beller, Sieghard; Bender, Andrea

    2015-01-01

    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. PMID:25620937

  11. 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.

  12. Spatial convergent cross mapping to detect causal relationships from short time series.

    PubMed

    Clark, Thomas; Ye, Hao; Isbell, Forest; Deyle, Ethan R; Cowles, Jane; Tilman, G David; Sugihara, George

    2015-05-01

    Recent developments in complex systems analysis have led to new techniques for detecting causal relationships using relatively short time series, on the order of 30 sequential observations. Although many ecological observation series are even shorter, perhaps fewer than ten sequential observations, these shorter time series are often highly replicated in space (i.e., plot replication). Here, we combine the existing techniques of convergent cross mapping (CCM) and dewdrop regression to build a novel test of causal relations that leverages spatial replication, which we call multispatial CCM. Using examples from simulated and real-world ecological data, we test the ability of multispatial CCM to detect causal relationships between processes. We find that multispatial CCM successfully detects causal relationships with as few as five sequential observations, even in the presence of process noise and observation error. Our results suggest that this technique may constitute a useful test for causality in systems where experiments are difficult to perform and long time series are not available. This new technique is available in the multispatialCCM package for the R programming language. PMID:26236832

  13. 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

  14. 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.

  15. 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

  16. Causal inference methods to study nonrandomized, preexisting development interventions.

    PubMed

    Arnold, Benjamin F; Khush, Ranjiv S; Ramaswamy, Padmavathi; London, Alicia G; Rajkumar, Paramasivan; Ramaprabha, Prabhakar; Durairaj, Natesan; Hubbard, Alan E; Balakrishnan, Kalpana; Colford, John M

    2010-12-28

    Empirical measurement of interventions to address significant global health and development problems is necessary to ensure that resources are applied appropriately. Such intervention programs are often deployed at the group or community level. The gold standard design to measure the effectiveness of community-level interventions is the community-randomized trial, but the conditions of these trials often make it difficult to assess their external validity and sustainability. The sheer number of community interventions, relative to randomized studies, speaks to a need for rigorous observational methods to measure their impact. In this article, we use the potential outcomes model for causal inference to motivate a matched cohort design to study the impact and sustainability of nonrandomized, preexisting interventions. We illustrate the method using a sanitation mobilization, water supply, and hygiene intervention in rural India. In a matched sample of 25 villages, we enrolled 1,284 children <5 y old and measured outcomes over 12 mo. Although we found a 33 percentage point difference in new toilet construction [95% confidence interval (CI) = 28%, 39%], we found no impacts on height-for-age Z scores (adjusted difference = 0.01, 95% CI = -0.15, 0.19) or diarrhea (adjusted longitudinal prevalence difference = 0.003, 95% CI = -0.001, 0.008) among children <5 y old. This study demonstrates that matched cohort designs can estimate impacts from nonrandomized, preexisting interventions that are used widely in development efforts. Interpreting the impacts as causal, however, requires stronger assumptions than prospective, randomized studies.

  17. Supporting inquiry learning by promoting normative understanding of multivariable causality

    NASA Astrophysics Data System (ADS)

    Keselman, Alla

    2003-11-01

    Early adolescents may lack the cognitive and metacognitive skills necessary for effective inquiry learning. In particular, they are likely to have a nonnormative mental model of multivariable causality in which effects of individual variables are neither additive nor consistent. Described here is a software-based intervention designed to facilitate students' metalevel and performance-level inquiry skills by enhancing their understanding of multivariable causality. Relative to an exploration-only group, sixth graders who practiced predicting an outcome (earthquake risk) based on multiple factors demonstrated increased attention to evidence, improved metalevel appreciation of effective strategies, and a trend toward consistent use of a controlled comparison strategy. Sixth graders who also received explicit instruction in making predictions based on multiple factors showed additional improvement in their ability to compare multiple instances as a basis for inferences and constructed the most accurate knowledge of the system. Gains were maintained in transfer tasks. The cognitive skills and metalevel understanding examined here are essential to inquiry learning.

  18. Creators' intentions bias judgments of function independently from causal inferences.

    PubMed

    Chaigneau, Sergio E; Castillo, Ramón D; Martínez, Luis

    2008-10-01

    Participants learned about novel artifacts that were created for function X, but later used for function Y. When asked to rate the extent to which X and Y were a given artifact's function, participants consistently rated X higher than Y. In Experiments 1 and 2, participants were also asked to rate artifacts' efficiency to perform X and Y. This allowed us to test if participants' preference for X was mediated by causal inferences. Experiment 1 showed that participants did not infer intentionally created artifacts performed X more efficiently than Y. Experiment 2 showed participants did not infer that only an efficient (but not an inefficient) artifact provided evidence of intentional creation. Causal inferences involving efficiency, did not account for participants' preferences. In Experiment 3, in contrast, when the creator changed her mind about an artifact's function (i.e., from X to Y), the preference for the original function tended to disappear. Creators' intentions were the basis for participants' preference. Results are discussed relative to essentialist theories.

  19. Reichenbach on causality in 1923: Scientific inference, coordination, and confirmation.

    PubMed

    Padovani, Flavia

    2015-10-01

    In The Theory of Relativity and A Priori Knowledge (1920b), Reichenbach developed an original account of cognition as coordination of formal structures to empirical ones. One of the most salient features of this account is that it is explicitly not a top-down type of coordination, and in fact it is crucially "directed" by the empirical side. Reichenbach called this feature "the mutuality of coordination" but, in that work, did not elaborate sufficiently on how this is supposed to work. In a paper that he wrote less than two years afterwards (but that he published only in 1932), "The Principle of Causality and the Possibility of its Empirical Confirmation" (1923/1932), he described what seems to be a model for this idea, now within an analysis of causality that results in an account of scientific inference. Recent reassessments of his early proposal do not seem to capture the extent of Reichenbach's original worries. The present paper analyses Reichenbach's early account and suggests a new way to look at his early work. According to it, we perform measurements, individuate parameters, collect and analyse data, by using a "constructive" approach, such as the one with which we formulate and test hypotheses, which paradigmatically requires some simplicity assumptions. Reichenbach's attempt to account for all these aspects in 1923 was obviously limited and naive in many ways, but it shows that, in his view, there were multiple ways in which the idea of "constitution" is embodied in scientific practice.

  20. Granger causality--statistical analysis under a configural perspective.

    PubMed

    von Eye, Alexander; Wiedermann, Wolfgang; Mun, Eun-Young

    2014-03-01

    The concept of Granger causality can be used to examine putative causal relations between two series of scores. Based on regression models, it is asked whether one series can be considered the cause for the second series. In this article, we propose extending the pool of methods available for testing hypotheses that are compatible with Granger causation by adopting a configural perspective. This perspective allows researchers to assume that effects exist for specific categories only or for specific sectors of the data space, but not for other categories or sectors. Configural Frequency Analysis (CFA) is proposed as the method of analysis from a configural perspective. CFA base models are derived for the exploratory analysis of Granger causation. These models are specified so that they parallel the regression models used for variable-oriented analysis of hypotheses of Granger causation. An example from the development of aggression in adolescence is used. The example shows that only one pattern of change in aggressive impulses over time Granger-causes change in physical aggression against peers.

  1. Reichenbach on causality in 1923: Scientific inference, coordination, and confirmation.

    PubMed

    Padovani, Flavia

    2015-10-01

    In The Theory of Relativity and A Priori Knowledge (1920b), Reichenbach developed an original account of cognition as coordination of formal structures to empirical ones. One of the most salient features of this account is that it is explicitly not a top-down type of coordination, and in fact it is crucially "directed" by the empirical side. Reichenbach called this feature "the mutuality of coordination" but, in that work, did not elaborate sufficiently on how this is supposed to work. In a paper that he wrote less than two years afterwards (but that he published only in 1932), "The Principle of Causality and the Possibility of its Empirical Confirmation" (1923/1932), he described what seems to be a model for this idea, now within an analysis of causality that results in an account of scientific inference. Recent reassessments of his early proposal do not seem to capture the extent of Reichenbach's original worries. The present paper analyses Reichenbach's early account and suggests a new way to look at his early work. According to it, we perform measurements, individuate parameters, collect and analyse data, by using a "constructive" approach, such as the one with which we formulate and test hypotheses, which paradigmatically requires some simplicity assumptions. Reichenbach's attempt to account for all these aspects in 1923 was obviously limited and naive in many ways, but it shows that, in his view, there were multiple ways in which the idea of "constitution" is embodied in scientific practice. PMID:26386525

  2. Causal Loop Analysis of coastal geomorphological systems

    NASA Astrophysics Data System (ADS)

    Payo, Andres; Hall, Jim W.; French, Jon; Sutherland, James; van Maanen, Barend; Nicholls, Robert J.; Reeve, Dominic E.

    2016-03-01

    As geomorphologists embrace ever more sophisticated theoretical frameworks that shift from simple notions of evolution towards single steady equilibria to recognise the possibility of multiple response pathways and outcomes, morphodynamic modellers are facing the problem of how to keep track of an ever-greater number of system feedbacks. Within coastal geomorphology, capturing these feedbacks is critically important, especially as the focus of activity shifts from reductionist models founded on sediment transport fundamentals to more synthesist ones intended to resolve emergent behaviours at decadal to centennial scales. This paper addresses the challenge of mapping the feedback structure of processes controlling geomorphic system behaviour with reference to illustrative applications of Causal Loop Analysis at two study cases: (1) the erosion-accretion behaviour of graded (mixed) sediment beds, and (2) the local alongshore sediment fluxes of sand-rich shorelines. These case study examples are chosen on account of their central role in the quantitative modelling of geomorphological futures and as they illustrate different types of causation. Causal loop diagrams, a form of directed graph, are used to distil the feedback structure to reveal, in advance of more quantitative modelling, multi-response pathways and multiple outcomes. In the case of graded sediment bed, up to three different outcomes (no response, and two disequilibrium states) can be derived from a simple qualitative stability analysis. For the sand-rich local shoreline behaviour case, two fundamentally different responses of the shoreline (diffusive and anti-diffusive), triggered by small changes of the shoreline cross-shore position, can be inferred purely through analysis of the causal pathways. Explicit depiction of feedback-structure diagrams is beneficial when developing numerical models to explore coastal morphological futures. By explicitly mapping the feedbacks included and neglected within a

  3. Population and growth causality in developing countries.

    PubMed

    Kapuria-foreman, V

    1995-07-01

    This study empirically tests the null hypotheses of no causality between population growth and economic growth and of no causality between economic growth and population growth in 15 developing countries. The model follows the Cheng Hsiao form with lag lengths to minimize Akaike's Final Prediction Error (FPE). Equations are run separately for each country. Lag lengths and Granger causality test were chosen according to three steps. 1) Each of the variables was regressed on its own lagged values with a maximum lag of five years. A lag length was chosen that minimized FPE, which was calculated for each regression. 2) Bivariate regressions were run with a fixed lag length for population growth and mixed lag lengths for the other variable, until the lag length which minimized FPE was determined. 3) The last step involved checking the lag length of population growth by keeping the lag fixed for economic growth. The economic growth measure was gross domestic product per capita. Findings indicate that in seven countries the null hypothesis of no causality between population growth and economic growth, either positive or negative, cannot be rejected (Ghana, Sri Lanka, Bolivia, Philippines, Syria, Thailand, and Argentina). In Nepal, India, China, Guatemala, Peru, Turkey, Chile, and Mexico lagged values of population growth improve predictions of economic growth. Higher economic growth has no significant effect on population growth rates in Nepal, Bolivia, Philippines, Guatemala, Peru, Thailand, Argentina, and Mexico. Interaction between economic growth and population growth was found in India, China, Turkey, and Chile. The direction of causation tests indicate that population growth has a significant positive impact on income growth in China, Guatemala, Turkey, Chile, and Mexico. India shows a negative impact of population growth on income. A significant negative impact of economic growth on population growth is evident only in Sri Lanka. There is weak evidence of a

  4. Rapidity Correlation Structures from Causal Hydrodynamics

    NASA Astrophysics Data System (ADS)

    Gavin, Sean; Moschelli, George; Zin, Christopher

    2016-08-01

    Viscous diffusion can broaden the rapidity dependence of two-particle transverse momentum fluctuations. Surprisingly, measurements at RHIC by the STAR collaboration demonstrate that this broadening is accompanied by the appearance of unanticipated structure in the rapidity distribution of these fluctuations in the most central collisions. Although a first order classical Navier-Stokes theory can roughly explain the rapidity broadening, it cannot explain the additional structure. We propose that the rapidity structure can be explained using the second order causal Israel-Stewart hydrodynamics with stochastic noise.

  5. 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

  6. 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

  7. 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.

  8. Extrinsic curvature in two-dimensional causal dynamical triangulation

    NASA Astrophysics Data System (ADS)

    Glaser, Lisa; Sotiriou, Thomas P.; Weinfurtner, Silke

    2016-09-01

    Causal dynamical triangulation (CDT) is a nonperturbative quantization of general relativity. Hořava-Lifshitz gravity, on the other hand, modifies general relativity to allow for perturbative quantization. Past work has given rise to the speculation that Hořava-Lifshitz gravity might correspond to the continuum limit of CDT. In this paper we add another piece to this puzzle by applying the CDT quantization prescription directly to Hořava-Lifshitz gravity in two dimensions. We derive the continuum Hamiltonian, and we show that it matches exactly the Hamiltonian derived from canonically quantizing the Hořava-Lifshitz action. Unlike the standard CDT case, here the introduction of a foliated lattice does not impose further restriction on the configuration space and, as a result, lattice quantization does not leave any imprint on continuum physics as expected.

  9. Mendelian Randomization for the Identification of Causal Pathways in Atherosclerotic Vascular Disease.

    PubMed

    Jansen, Henning; Lieb, Wolfgang; Schunkert, Heribert

    2016-02-01

    Epidemiological and clinical studies have identified many physiological traits and biomarkers that are statistically associated with coronary artery disease (CAD). For some of these traits and biomarkers it is well established that they represent true causal risk factors for CAD. For other biomarkers, however, the distinct character of association is still a matter of debate. Randomized controlled trials (RCT) had a pivotal role in establishing causal associations between risk factors and biomarkers and CAD in some settings by demonstrating that therapeutic intervention targeting risk factors/biomarkers also affect the risk for clinical outcomes, such as CAD. In other scenarios, however, RCTs did not demonstrate clear benefits associated with lowering biomarker levels and therefore suggest that the association between these biomarkers (like C reactive protein) and CAD was driven by confounding or reverse causation. Even accurately conducted RCTs are not immune against incorrect causal inference. Moreover, the extensive costs and efforts required to conduct RCTs asked for alternative study designs to elucidate potential causal associations. Mendelian Randomization studies represent one such alternative by using genetic variants as proxies for specific biomarkers to investigate potential causal relations between biomarkers and clinical outcomes. In this review, we briefly describe the principles of MR studies and summarize recent MR studies in the context of CAD. PMID:26791863

  10. 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.

  11. Investigating effective brain connectivity from fMRI data: past findings and current issues with reference to Granger causality analysis.

    PubMed

    Deshpande, Gopikrishna; Hu, Xiaoping

    2012-01-01

    Interactions between brain regions have been recognized as a critical ingredient required to understand brain function. Two modes of interactions have held prominence-synchronization and causal influence. Efforts to ascertain causal influence from functional magnetic resonance imaging (fMRI) data have relied primarily on confirmatory model-driven approaches, such as dynamic causal modeling and structural equation modeling, and exploratory data-driven approaches such as Granger causality analysis. A slew of recent articles have focused on the relative merits and caveats of these approaches. The relevant studies can be classified into simulations, theoretical developments, and experimental results. In the first part of this review, we will consider each of these themes and critically evaluate their arguments, with regard to Granger causality analysis. Specifically, we argue that simulations are bounded by the assumptions and simplifications made by the simulator, and hence must be regarded only as a guide to experimental design and should not be viewed as the final word. On the theoretical front, we reason that each of the improvements to existing, yet disparate, methods brings them closer to each other with the hope of eventually leading to a unified framework specifically designed for fMRI. We then review latest experimental results that demonstrate the utility and validity of Granger causality analysis under certain experimental conditions. In the second part, we will consider current issues in causal connectivity analysis-hemodynamic variability, sampling, instantaneous versus causal relationship, and task versus resting states. We highlight some of our own work regarding these issues showing the effect of hemodynamic variability and sampling on Granger causality. Further, we discuss recent techniques such as the cubature Kalman filtering, which can perform blind deconvolution of the hemodynamic response robustly well, and hence enabling wider application of

  12. Investigating Effective Brain Connectivity from fMRI Data: Past Findings and Current Issues with Reference to Granger Causality Analysis

    PubMed Central

    2012-01-01

    Abstract Interactions between brain regions have been recognized as a critical ingredient required to understand brain function. Two modes of interactions have held prominence—synchronization and causal influence. Efforts to ascertain causal influence from functional magnetic resonance imaging (fMRI) data have relied primarily on confirmatory model-driven approaches, such as dynamic causal modeling and structural equation modeling, and exploratory data-driven approaches such as Granger causality analysis. A slew of recent articles have focused on the relative merits and caveats of these approaches. The relevant studies can be classified into simulations, theoretical developments, and experimental results. In the first part of this review, we will consider each of these themes and critically evaluate their arguments, with regard to Granger causality analysis. Specifically, we argue that simulations are bounded by the assumptions and simplifications made by the simulator, and hence must be regarded only as a guide to experimental design and should not be viewed as the final word. On the theoretical front, we reason that each of the improvements to existing, yet disparate, methods brings them closer to each other with the hope of eventually leading to a unified framework specifically designed for fMRI. We then review latest experimental results that demonstrate the utility and validity of Granger causality analysis under certain experimental conditions. In the second part, we will consider current issues in causal connectivity analysis—hemodynamic variability, sampling, instantaneous versus causal relationship, and task versus resting states. We highlight some of our own work regarding these issues showing the effect of hemodynamic variability and sampling on Granger causality. Further, we discuss recent techniques such as the cubature Kalman filtering, which can perform blind deconvolution of the hemodynamic response robustly well, and hence enabling wider

  13. Causal beliefs about intellectual disability and schizophrenia and their relationship with awareness of the condition and social distance.

    PubMed

    Scior, Katrina; Furnham, Adrian

    2016-09-30

    Evidence on mental illness stigma abounds yet little is known about public perceptions of intellectual disability. This study examined causal beliefs about intellectual disability and schizophrenia and how these relate to awareness of the condition and social distance. UK lay people aged 16+(N=1752), in response to vignettes depicting intellectual disability and schizophrenia, noted their interpretation of the difficulties, and rated their agreement with 22 causal and four social distance items. They were most likely to endorse environmental causes for intellectual disability, and biomedical factors, trauma and early disadvantage for schizophrenia. Accurate identification of both vignettes was associated with stronger endorsement of biomedical causes, alongside weaker endorsement of adversity, environmental and supernatural causes. Biomedical causal beliefs and social distance were negatively correlated for intellectual disability, but not for schizophrenia. Causal beliefs mediated the relationship between identification of the condition and social distance for both conditions. While all four types of causal beliefs acted as mediators for intellectual disability, for schizophrenia only supernatural causal beliefs did. Educating the public and promoting certain causal beliefs may be of benefit in tackling intellectual disability stigma, but for schizophrenia, other than tackling supernatural attributions, may be of little benefit in reducing stigma. PMID:27376670

  14. Causal beliefs about intellectual disability and schizophrenia and their relationship with awareness of the condition and social distance.

    PubMed

    Scior, Katrina; Furnham, Adrian

    2016-09-30

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

  15. 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.

  16. A diagnostic method that uses causal knowledge and linear programming in the application of Bayes' formula.

    PubMed

    Cooper, G F

    1986-04-01

    Bayes' formula has been applied extensively in computer-based medical diagnostic systems. One assumption that is often made in the application of the formula is that the findings in a case are conditionally independent. This assumption is often invalid and leads to inaccurate posterior probability assignments to the diagnostic hypotheses. This paper discusses a method for using causal knowledge to structure findings according to their probabilistic dependencies. An inference procedure is discussed which propagates probabilities within a network of causally related findings in order to calculate posterior probabilities of diagnostic hypotheses. A linear programming technique is described that bounds the values of the propagated probabilities subject to known probabilistic constraints.

  17. 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.

  18. Bulk viscosity and relaxation time of causal dissipative relativistic fluid dynamics

    NASA Astrophysics Data System (ADS)

    Huang, Xu-Guang; Kodama, Takeshi; Koide, Tomoi; Rischke, Dirk H.

    2011-02-01

    The microscopic formulas of the bulk viscosity ζ and the corresponding relaxation time τΠ in causal dissipative relativistic fluid dynamics are derived by using the projection operator method. In applying these formulas to the pionic fluid, we find that the renormalizable energy-momentum tensor should be employed to obtain consistent results. In the leading-order approximation in the chiral perturbation theory, the relaxation time is enhanced near the QCD phase transition, and τΠ and ζ are related as τΠ=ζ/[β{(1/3-cs2)(ɛ+P)-2(ɛ-3P)/9}], where ɛ, P, and cs are the energy density, pressure, and velocity of sound, respectively. The predicted ζ and τΠ should satisfy the so-called causality condition. We compare our result with the results of the kinetic calculation by Israel and Stewart and the string theory, and confirm that all three approaches are consistent with the causality condition.

  19. Consistent theory for causal non-locality beyond the Born's rule

    NASA Astrophysics Data System (ADS)

    Son, Wonmin

    2014-02-01

    According to the theory of relativity and causality, a special type of correlation beyond quantum mechanics is possible in principle under the name of a non-local box. The concept has been introduced from the principle of non-locality, which satisfies relativistic causality. In this paper, we show that a correlation leading to the non-local box can be derived consistently if we release one of major axioms in quantum mechanics, Born's rule. This allows us to obtain a theory that in one end of the spectrum agrees with the classical probability and in the other end agrees with the theory of non-local causality. At the same time, we argue that the correlation lies in a space with special mathematical constraints such that a physical realization of the correlation through a probability measure is not possible in one direction of its limit, but is possible in the other limit.

  20. N250 effects for letter transpositions depend on lexicality: 'casual' or 'causal'?

    PubMed

    Duñabeitia, Jon Andoni; Molinaro, Nicola; Laka, Itziar; Estévez, Adelina; Carreiras, Manuel

    2009-03-01

    We examined the electrophysiological correlates of one of the most influential orthographic effects: the transposed-letter-masked priming effect. Transposed-letter nonword-word pairs ('jugde-judge'), as well as transposed-letter word-word pairs ('casual-causal') were included to investigate the influence of prime's lexicality in the transposed-letter effect. The results showed that when compared with the substituted-letter control conditions ('jugde-judge' vs. 'jupte-judge'), transposed-letter primes produced a lower negativity in the N250 component. In contrast, no differences were obtained between the two word-word priming conditions ('casual-causal' vs. 'carnal-causal'). The influence of lexicality in the transposed-letter effect is discussed according to the models of visual word recognition and previous evidence from event-related potentials.

  1. When learning order affects sensitivity to base rates: challenges for theories of causal learning.

    PubMed

    Reips, Ulf-Dietrich; Waldmann, Michael R

    2008-01-01

    In three experiments we investigated whether two procedures of acquiring knowledge about the same causal structure, predictive learning (from causes to effects) versus diagnostic learning (from effects to causes), would lead to different base-rate use in diagnostic judgments. Results showed that learners are capable of incorporating base-rate information in their judgments regardless of the direction in which the causal structure is learned. However, this only holds true for relatively simple scenarios. When complexity was increased, base rates were only used after diagnostic learning, but were largely neglected after predictive learning. It could be shown that this asymmetry is not due to a failure of encoding base rates in predictive learning because participants in all conditions were fairly good at reporting them. The findings present challenges for all theories of causal learning.

  2. Causality at the dawn of the 'omics' era in medicine and in nephrology.

    PubMed

    Zoccali, Carmine; Brancaccio, Diego; Nathan, Marco J

    2016-09-01

    Causality is a core concept in medicine. The quantitative determinacy characterizing today's biomedical science is unprecedented. The assessment of causal relations in human diseases is evolving, and it is therefore fundamental to keep up with the steady pace of theoretical and technological advancements. The exact specification of all causes of pathologies at the individual level, precision medicine, is expected to allow the complete eradication of disease. In this article, we discuss the various conceptualizations of causation that are at play in the context of randomized clinical trials and observational studies. Genomics, proteomics, metabolomics and epigenetics can now produce the precise knowledge we need for 21st century medicine. New conceptions of causality are needed to form the basis of the new precision medicine.

  3. Propagation of electromagnetic waves in resistive pair plasma and causal relativistic magnetohydrodynamics

    SciTech Connect

    Koide, Shinji

    2008-12-15

    We investigate the propagation of electromagnetic waves in resistive e{sup {+-}} pair plasmas using a one-fluid theory derived from the relativistic two-fluid equations. When the resistivity normalized by the electron/positron inertia variable exceeds a critical value, the dispersion relation for electromagnetic waves shows that the group velocity is larger than the light speed in vacuum. However, in such a case, it also is found that the plasma parameter is less than unity: that is, the electron-positron pair medium no longer can be treated as plasma. Thus, the simple two-fluid approximation is invalid. This confirms that superluminal propagation of electromagnetic wave is forbidden in a plasma--a conclusion consistent with the relativistic principle of causality. As an alternative, we propose a new set of equations for ''causal relativistic magnetohydrodynamics,'' which both have nonzero resistivity and yet are consistent with the causality principle.

  4. Selecting causal genes from genome-wide association studies via functionally-coherent subnetworks

    PubMed Central

    Taşan, Murat; Musso, Gabriel; Hao, Tong; Vidal, Marc; MacRae, Calum A.; Roth, Frederick P.

    2015-01-01

    While genome-wide association (GWA) studies have linked thousands of loci to human diseases, the causal genes and variants at these loci generally remain unknown. Although investigators typically focus on genes closest to the associated polymorphisms, the causal gene is often more distal. Relying on the literature to help prioritize additional candidate genes at associated loci can draw attention away from less-characterized causal genes. Here we describe a strategy that uses genome-scale ‘co-function’ networks to identify sets of mutually functionally related genes spanning multiple GWA loci. Using associations from ~100 GWA studies covering ten cancer types, this approach outperforms the common alternative strategy in ranking known cancer genes. The strategy’s power grows with more GWA loci, offering an increasing opportunity to elucidate causes of complex human disease. PMID:25532137

  5. Causality at the dawn of the 'omics' era in medicine and in nephrology.

    PubMed

    Zoccali, Carmine; Brancaccio, Diego; Nathan, Marco J

    2016-09-01

    Causality is a core concept in medicine. The quantitative determinacy characterizing today's biomedical science is unprecedented. The assessment of causal relations in human diseases is evolving, and it is therefore fundamental to keep up with the steady pace of theoretical and technological advancements. The exact specification of all causes of pathologies at the individual level, precision medicine, is expected to allow the complete eradication of disease. In this article, we discuss the various conceptualizations of causation that are at play in the context of randomized clinical trials and observational studies. Genomics, proteomics, metabolomics and epigenetics can now produce the precise knowledge we need for 21st century medicine. New conceptions of causality are needed to form the basis of the new precision medicine. PMID:27190363

  6. God Does Not Play Dice: Causal Determinism and Preschoolers' Causal Inferences

    ERIC Educational Resources Information Center

    Schulz, Laura E.; Sommerville, Jessica

    2006-01-01

    Three studies investigated children's belief in causal determinism. If children are determinists, they should infer unobserved causes whenever observed causes appear to act stochastically. In Experiment 1, 4-year-olds saw a stochastic generative cause and inferred the existence of an unobserved inhibitory cause. Children traded off inferences…

  7. Interference between Cues Requires a Causal Scenario: Favorable Evidence for Causal Reasoning Models in Learning Processes

    ERIC Educational Resources Information Center

    Luque, David; Cobos, Pedro L.; Lopez, Francisco J.

    2008-01-01

    In an interference-between-cues design (IbC), the expression of a learned Cue A-Outcome 1 association has been shown to be impaired if another cue, B, is separately paired with the same outcome in a second learning phase. The present study examined whether IbC could be caused by associative mechanisms independent of causal reasoning processes.…

  8. Temporal predictability enhances judgements of causality in elemental causal induction from both observation and intervention.

    PubMed

    Greville, W James; Buehner, Marc J

    2016-01-01

    When the temporal interval or delay separating cause and effect is consistent over repeated instances, it becomes possible to predict when the effect will follow from the cause, hence temporal predictability serves as an appropriate term for describing consistent cause-effect delays. It has been demonstrated that in instrumental action-outcome learning tasks, enhancing temporal predictability by holding the cause-effect interval constant elicits higher judgements of causality compared to conditions involving variable temporal intervals. Here, we examine whether temporal predictability exerts a similar influence when causal learning takes place through observation rather than intervention through instrumental action. Four experiments demonstrated that judgements of causality were higher when the temporal interval was constant than when it was variable, and that judgements declined with increasing variability. We further found that this beneficial effect of predictability was stronger in situations where the effect base-rate was zero (Experiments 1 and 3). The results therefore clearly indicate that temporal predictability enhances impressions of causality, and that this effect is robust and general. Factors that could mediate this effect are discussed.

  9. 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…

  10. Demographics, Causality, Work Salience, and the Career Maturity of African-American Students: A Causal Model.

    ERIC Educational Resources Information Center

    Naidoo, Anthony V.; Bowman, Sharon L.; Gerstein, Lawrence H.

    1998-01-01

    A model proposing that causality and work salience moderate the influence of gender, educational level, and socioeconomic status on career maturity was tested with 288 African-American students. Work salience had the strongest direct effect on career maturity. For these students home/family had higher salience than did work. (SK)

  11. Evidence for online processing during causal learning.

    PubMed

    Liu, Pei-Pei; Luhmann, Christian C

    2015-03-01

    Many models of learning describe both the end product of learning (e.g., causal judgments) and the cognitive mechanisms that unfold on a trial-by-trial basis. However, the methods employed in the literature typically provide only indirect evidence about the unfolding cognitive processes. Here, we utilized a simultaneous secondary task to measure cognitive processing during a straightforward causal-learning task. The results from three experiments demonstrated that covariation information is not subject to uniform cognitive processing. Instead, we observed systematic variation in the processing dedicated to individual pieces of covariation information. In particular, observations that are inconsistent with previously presented covariation information appear to elicit greater cognitive processing than do observations that are consistent with previously presented covariation information. In addition, the degree of cognitive processing appears to be driven by learning per se, rather than by nonlearning processes such as memory and attention. Overall, these findings suggest that monitoring learning processes at a finer level may provide useful psychological insights into the nature of learning.

  12. Causal mechanisms in airfoil-circulation formation

    NASA Astrophysics Data System (ADS)

    Zhu, J. Y.; Liu, T. S.; Liu, L. Q.; Zou, S. F.; Wu, J. Z.

    2015-12-01

    In this paper, we trace the dynamic origin, rather than any kinematic interpretations, of lift in two-dimensional flow to the physical root of airfoil circulation. We show that the key causal process is the vorticity creation by tangent pressure gradient at the airfoil surface via no-slip condition, of which the theoretical basis has been given by Lighthill ["Introduction: Boundary layer theory," in Laminar Boundary Layers, edited by L. Rosenhead (Clarendon Press, 1963), pp. 46-113], which we further elaborate. This mechanism can be clearly revealed in terms of vorticity formulation but is hidden in conventional momentum formulation, and hence has long been missing in the history of one's efforts to understand lift. By a careful numerical simulation of the flow around a NACA-0012 airfoil, and using both Eulerian and Lagrangian descriptions, we illustrate the detailed transient process by which the airfoil gains its circulation and demonstrate the dominating role of relevant dynamical causal mechanisms at the boundary. In so doing, we find that the various statements for the establishment of Kutta condition in steady inviscid flow actually correspond to a sequence of events in unsteady viscous flow.

  13. Causal Inference for Spatial Constancy across Saccades.

    PubMed

    Atsma, Jeroen; Maij, Femke; Koppen, Mathieu; Irwin, David E; Medendorp, W Pieter

    2016-03-01

    Our ability to interact with the environment hinges on creating a stable visual world despite the continuous changes in retinal input. To achieve visual stability, the brain must distinguish the retinal image shifts caused by eye movements and shifts due to movements of the visual scene. This process appears not to be flawless: during saccades, we often fail to detect whether visual objects remain stable or move, which is called saccadic suppression of displacement (SSD). How does the brain evaluate the memorized information of the presaccadic scene and the actual visual feedback of the postsaccadic visual scene in the computations for visual stability? Using a SSD task, we test how participants localize the presaccadic position of the fixation target, the saccade target or a peripheral non-foveated target that was displaced parallel or orthogonal during a horizontal saccade, and subsequently viewed for three different durations. Results showed different localization errors of the three targets, depending on the viewing time of the postsaccadic stimulus and its spatial separation from the presaccadic location. We modeled the data through a Bayesian causal inference mechanism, in which at the trial level an optimal mixing of two possible strategies, integration vs. separation of the presaccadic memory and the postsaccadic sensory signals, is applied. Fits of this model generally outperformed other plausible decision strategies for producing SSD. Our findings suggest that humans exploit a Bayesian inference process with two causal structures to mediate visual stability. PMID:26967730

  14. Emergent horizons and causal structures in holography

    NASA Astrophysics Data System (ADS)

    Banerjee, Avik; Kundu, Arnab; Kundu, Sandipan

    2016-09-01

    The open string metric arises kinematically in studying fluctuations of open string degrees of freedom on a D-brane. An observer, living on a probe D-brane, can send signals through the spacetime by using such fluctuations on the probe, that propagate in accordance with a metric which is conformal to the open string metric. Event horizons can emerge in the open string metric when one considers a D-brane with an electric field on its worldvolume. Here, we emphasize the role of and investigate, in details, the causal structure of the resulting open string event horizon and demonstrate, among other things, its close similarities to an usual black hole event horizon in asymptotically AdS-spaces. To that end, we analyze relevant geodesics, Penrose diagrams and various causal holographic observables for a given open string metric. For analytical control, most of our calculations are performed in an asymptotically AdS3-background, however, we argue that the physics is qualitatively the same in higher dimensions. We also discuss how this open string metric arises from an underlying D-brane configuration in string theory.

  15. Causal Drift, Robust Signaling, and Complex Disease

    PubMed Central

    Wagner, Andreas

    2015-01-01

    The phenotype of many regulatory circuits in which mutations can cause complex, polygenic diseases is to some extent robust to DNA mutations that affect circuit components. Here I demonstrate how such mutational robustness can prevent the discovery of genetic disease determinants. To make my case, I use a mathematical model of the insulin signaling pathway implicated in type 2 diabetes, whose signaling output is governed by 15 genetically determined parameters. Using multiple complementary measures of a parameter’s importance for this phenotype, I show that any one disease determinant that is crucial in one genetic background will be virtually irrelevant in other backgrounds. In an evolving population that drifts through the parameter space of this or other robust circuits through DNA mutations, the genetic changes that can cause disease will vary randomly over time. I call this phenomenon causal drift. It means that mutations causing disease in one (human or non-human) population may have no effect in another population, and vice versa. Causal drift casts doubt on our ability to infer the molecular mechanisms of complex diseases from non-human model organisms. PMID:25774510

  16. Comparison Analysis: Granger Causality and New Causality and Their Applications to Motor Imagery.

    PubMed

    Hu, Sanqing; Wang, Hui; Zhang, Jianhai; Kong, Wanzeng; Cao, Yu; Kozma, Robert

    2016-07-01

    In this paper we first point out a fatal drawback that the widely used Granger causality (GC) needs to estimate the autoregressive model, which is equivalent to taking a series of backward recursive operations which are infeasible in many irreversible chemical reaction models. Thus, new causality (NC) proposed by Hu et al. (2011) is theoretically shown to be more sensitive to reveal true causality than GC. We then apply GC and NC to motor imagery (MI) which is an important mental process in cognitive neuroscience and psychology and has received growing attention for a long time. We study causality flow during MI using scalp electroencephalograms from nine subjects in Brain-computer interface competition IV held in 2008. We are interested in three regions: Cz (central area of the cerebral cortex), C3 (left area of the cerebral cortex), and C4 (right area of the cerebral cortex) which are considered to be optimal locations for recognizing MI states in the literature. Our results show that: 1) there is strong directional connectivity from Cz to C3/C4 during left- and right-hand MIs based on GC and NC; 2) during left-hand MI, there is directional connectivity from C4 to C3 based on GC and NC; 3) during right-hand MI, there is strong directional connectivity from C3 to C4 which is much clearly revealed by NC than by GC, i.e., NC largely improves the classification rate; and 4) NC is demonstrated to be much more sensitive to reveal causal influence between different brain regions than GC.

  17. Material Phase Causality or a Dynamics-Statistical Interpretation of Quantum Mechanics

    SciTech Connect

    Koprinkov, I. G.

    2010-11-25

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

  18. Foreign Language Listening Anxiety and Listening Performance: Conceptualizations and Causal Relationships

    ERIC Educational Resources Information Center

    Zhang, Xian

    2013-01-01

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

  19. Higher Education and Unemployment: A Cointegration and Causality Analysis of the Case of Turkey

    ERIC Educational Resources Information Center

    Erdem, Ekrem; Tugcu, Can Tansel

    2012-01-01

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

  20. Just Do It? Investigating the Gap between Prediction and Action in Toddlers Causal Inferences

    ERIC Educational Resources Information Center

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

    2010-01-01

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

  1. Processing Inferential Causal Statements: Theoretical Refinements and the Role of Verb Type

    ERIC Educational Resources Information Center

    Mohamed, Mohamed Taha; Clifton, Charles, Jr.

    2008-01-01

    An evidential causal relation like, "Because most distinguished students got bad grades, the teacher made some mistakes in evaluating his students' papers," is more difficult to process than a factual one like, "Because he got tired after a long semester, the teacher made some mistakes in evaluating his students' papers" (Noordman & de Blijzer,…

  2. A Longitudinal Developmental Analysis of Students' Causality Beliefs about School Performance

    ERIC Educational Resources Information Center

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

    2014-01-01

    This study examined the development of school-related causality beliefs which are children's generalized perceptions of the utility or power of different categories of specific means in producing school outcomes. Based on the action theory perspective, we analyzed the developmental model of these beliefs as well as the trajectories of the…

  3. A Beta Index to Confirm Causal Directions in a Closed System of Five Variables.

    ERIC Educational Resources Information Center

    Nigro, George A.

    The beta coefficient of an intermediate variable in a causal direction remains relatively constant as other system variables are introduced and controlled in stepped regression, whereas that in the acausal direction changes noticeably. Normalized random numbers (200x5) were generated and substituted in interdependent equations to produce five…

  4. The Causal Effect of Education on Health: Evidence from the United Kingdom

    ERIC Educational Resources Information Center

    Silles, Mary A.

    2009-01-01

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

  5. A definition of causal effect for epidemiological research.

    PubMed

    Hernán, M A

    2004-04-01

    Estimating the causal effect of some exposure on some outcome is the goal of many epidemiological studies. This article reviews a formal definition of causal effect for such studies. For simplicity, the main description is restricted to dichotomous variables and assumes that no random error attributable to sampling variability exists. The appendix provides a discussion of sampling variability and a generalisation of this causal theory. The difference between association and causation is described-the redundant expression "causal effect" is used throughout the article to avoid confusion with a common use of "effect" meaning simply statistical association-and shows why, in theory, randomisation allows the estimation of causal effects without further assumptions. The article concludes with a discussion on the limitations of randomised studies. These limitations are the reason why methods for causal inference from observational data are needed.

  6. Air Pollution and Autism Spectrum Disorders: Causal or Confounded?

    PubMed

    Weisskopf, Marc G; Kioumourtzoglou, Marianthi-Anna; Roberts, Andrea L

    2015-12-01

    In the last decade, several studies have examined the association between perinatal exposure to ambient air pollution and risk of autism spectrum disorder (ASD). These studies have largely been consistent, with associations seen with different aspects of air pollution, including hazardous air toxics, ozone, particulate, and traffic-related pollution. Confounding by socioeconomic status (SES) and place of residence are of particular concern, as these can be related to ASD case ascertainment and other potential causal risk factors for ASD. While all studies take steps to address this concern, residual confounding is difficult to rule out. Two recent studies of air pollution and ASD, however, present findings that strongly argue against residual confounding, especially for factors that do not vary over relatively short time intervals. These two studies, conducted in communities around the USA, found a specific association with air pollution exposure during the 3rd, but not the 1st, trimester, when both trimesters were modeled simultaneously. In this review, we discuss confounding possibilities and then explain-with the aid of directed acyclic graphs (DAGs)-why an association that is specific to a particular time window, when multiple exposure windows are simultaneously assessed, argues against residual confounding by (even unmeasured) non-time-varying factors. In addition, we discuss why examining ambient air pollution concentration as a proxy for personal exposure helps avoid confounding by personal behavior differences, and the implications of measurement error in using ambient concentrations as a proxy for personal exposures. Given the general consistency of findings across studies and the exposure-window-specific associations recently reported, the overall evidence for a causal association between air pollution and ASD is increasingly compelling.

  7. A Historical Overview and Contemporary Expansion of Psychological Theories of Determinism, Probabilistic Causality, Indeterminate Free Will, and Moral and Legal Responsibility

    ERIC Educational Resources Information Center

    Wilks, Duffy; Ratheal, Juli D'Ann

    2009-01-01

    The authors provide a historical overview of the development of contemporary theories of counseling and psychology in relation to determinism, probabilistic causality, indeterminate free will, and moral and legal responsibility. They propose a unique model of behavioral causality that incorporates a theory of indeterminate free will, a concept…

  8. Causal inference and the hierarchical structure of experience

    PubMed Central

    Johnson, Samuel G. B.; Keil, Frank C.

    2014-01-01

    Children and adults make rich causal inferences about the physical and social world, even in novel situations where they cannot rely on prior knowledge of causal mechanisms. We propose that this capacity is supported in part by constraints provided by event structure—the cognitive organization of experience into discrete events that are hierarchically organized. These event-structured causal inferences are guided by a level-matching principle, with events conceptualized at one level of an event hierarchy causally matched to other events at that same level, and a boundary-blocking principle, with events causally matched to other events that are parts of the same superordinate event. These principles are used to constrain inferences about plausible causal candidates in unfamiliar situations, both in diagnosing causes (Experiment 1) and predicting effects (Experiment 2). The results could not be explained by construal level (Experiment 3) or similarity-matching (Experiment 4), and were robust across a variety of physical and social causal systems. Taken together, these experiments demonstrate a novel way in which non-causal information we extract from the environment can help to constrain inferences about causal structure. PMID:25347533

  9. 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.

  10. Comment on ``Causality-violating Higgs singlets at the LHC''

    NASA Astrophysics Data System (ADS)

    Gielen, Steffen

    2013-09-01

    The spacetime of Ho and Weiler [Phys. Rev. D 87, 045004 (2013)] supposedly admitting closed timelike curves (CTCs) is flat Minkowski spacetime with a compactified coordinate and can only contain CTCs if the compact direction is chosen to be timelike. This case of a “periodic time” is probably the simplest example of a causality-violating spacetime; it trivially satisfies all energy conditions usually assumed in general relativity, and its geodesics are just straight lines. Its relevance for phenomenology of the LHC, on the other hand, depends on consistency with observational constraints on gravity, as is mentioned in general but not discussed in any detail by Ho and Weiler. We verify a basic consistency check for stationary sources.

  11. Causal Inference Considerations for Endocrine Disruptor Research in Children's Health

    PubMed Central

    Engel, Stephanie M.; Wolff, Mary S.

    2014-01-01

    Substantial population exposure to endocrine disrupting chemicals, combined with available biomarkers and public concern, has resulted in an explosion of human health effects research. At the same time, remarkable shifts in the regulations governing the composition of some consumer products that contain endocrine disruptors (EDs) has occurred. However, important questions remain as to the weight of evidence linking EDs to human health end points. In this review, we critically examine the literature linking ED exposures to child neurodevelopment, focusing in particular on two model exposures to demonstrate issues related to bioaccumulative [e.g., polychlorinated biphenyls (PCBs)] and rapidly metabolized (e.g., phthalates) compounds, respectively. Issues of study design, confounding, and exposure measurement are considered. Given widespread exposure to these compounds, the potential public health consequences of even small effects on human health are substantial. Therefore, advancing our understanding of any impact calls for careful attention to the principles of causal inference. PMID:23514318

  12. Information causality in the quantum and post-quantum regime.

    PubMed

    Ringbauer, Martin; Fedrizzi, Alessandro; Berry, Dominic W; White, Andrew G

    2014-01-01

    Quantum correlations can be stronger than anything achieved by classical systems, yet they are not reaching the limit imposed by relativity. The principle of information causality offers a possible explanation for why the world is quantum and why there appear to be no even stronger correlations. Generalizing the no-signaling condition it suggests that the amount of accessible information must not be larger than the amount of transmitted information. Here we study this principle experimentally in the classical, quantum and post-quantum regimes. We simulate correlations that are stronger than allowed by quantum mechanics by exploiting the effect of polarization-dependent loss in a photonic Bell-test experiment. Our method also applies to other fundamental principles and our results highlight the special importance of anisotropic regions of the no-signalling polytope in the study of fundamental principles. PMID:25378182

  13. Information causality in the quantum and post-quantum regime.

    PubMed

    Ringbauer, Martin; Fedrizzi, Alessandro; Berry, Dominic W; White, Andrew G

    2014-11-07

    Quantum correlations can be stronger than anything achieved by classical systems, yet they are not reaching the limit imposed by relativity. The principle of information causality offers a possible explanation for why the world is quantum and why there appear to be no even stronger correlations. Generalizing the no-signaling condition it suggests that the amount of accessible information must not be larger than the amount of transmitted information. Here we study this principle experimentally in the classical, quantum and post-quantum regimes. We simulate correlations that are stronger than allowed by quantum mechanics by exploiting the effect of polarization-dependent loss in a photonic Bell-test experiment. Our method also applies to other fundamental principles and our results highlight the special importance of anisotropic regions of the no-signalling polytope in the study of fundamental principles.

  14. D-Sitter Space: Causal Structure, Thermodynamics, and Entropy

    SciTech Connect

    Silverstein, Eva M

    2003-05-05

    We study the entropy of concrete de Sitter flux compactifications and deformations of them containing D-brane domain walls. We determine the relevant causal and thermodynamic properties of these ''D-Sitter'' deformations of de Sitter spacetimes. We find a string scale correspondence point at which the entropy localized on the D-branes (and measured by probes sent from an observer in the middle of the bubble) scales the same with large flux quantum numbers as the entropy of the original de Sitter space, and at which Bousso's bound is saturated by the D-brane degrees of freedom (up to order one coefficients) for an infinite range of times. From the geometry of a static patch of D-Sitter space and from basic relations in flux compactifications, we find support for the possibility of a low energy open string description of the static patch of de Sitter space.

  15. 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

  16. 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

  17. Applying Causal Reasoning to Analyze Value Systems

    NASA Astrophysics Data System (ADS)

    Macedo, Patrícia; Camarinha-Matos, Luis M.

    Collaborative networked organizations are composed of heterogeneous and autonomous entities. Thus it is natural that each member has its own set of values and preferences, as a result, conflicts among partners might emerge due to some values misalignment. Therefore, tools to support the analysis of Value Systems in a collaborative context are relevant to improve the network management. Since a Value System reflects the set of values and preferences of an actor, which are cognitive issues, a cognitive approach based on qualitative causal maps is suggested. Qualitative inference methods are presented in order to assess the potential for conflicts among network members and the positive impact between members' Value Systems. The software tool developed, in order to support the proposed framework and the qualitative inference methods, is briefly presented.

  18. Equity Theory Ratios as Causal Schemas.

    PubMed

    Arvanitis, Alexios; Hantzi, Alexandra

    2016-01-01

    Equity theory approaches justice evaluations based on ratios of exchange inputs to exchange outcomes. Situations are evaluated as just if ratios are equal and unjust if unequal. We suggest that equity ratios serve a more fundamental cognitive function than the evaluation of justice. More particularly, we propose that they serve as causal schemas for exchange outcomes, that is, they assist in determining whether certain outcomes are caused by inputs of other people in the context of an exchange process. Equality or inequality of ratios in this sense points to an exchange process. Indeed, Study 1 shows that different exchange situations, such as disproportional or balanced proportional situations, create perceptions of give-and-take on the basis of equity ratios. Study 2 shows that perceptions of justice are based more on communicatively accepted rules of interaction than equity-based evaluations, thereby offering a distinction between an attribution and an evaluation cognitive process for exchange outcomes. PMID:27594846

  19. Causality constraints in conformal field theory

    NASA Astrophysics Data System (ADS)

    Hartman, Thomas; Jain, Sachin; Kundu, Sandipan

    2016-05-01

    Causality places nontrivial constraints on QFT in Lorentzian signature, for example fixing the signs of certain terms in the low energy Lagrangian. In d dimensional conformal field theory, we show how such constraints are encoded in crossing symmetry of Euclidean correlators, and derive analogous constraints directly from the conformal bootstrap (analytically). The bootstrap setup is a Lorentzian four-point function corresponding to propagation through a shockwave. Crossing symmetry fixes the signs of certain log terms that appear in the conformal block expansion, which constrains the interactions of low-lying operators. As an application, we use the bootstrap to rederive the well known sign constraint on the (∂ ϕ)4 coupling in effective field theory, from a dual CFT. We also find constraints on theories with higher spin conserved currents. Our analysis is restricted to scalar correlators, but we argue that similar methods should also impose nontrivial constraints on the interactions of spinning operators.

  20. Causal structure and electrodynamics on Finsler spacetimes

    NASA Astrophysics Data System (ADS)

    Pfeifer, Christian; Wohlfarth, Mattias N. R.

    2011-08-01

    We present a concise new definition of Finsler spacetimes that generalizes Lorentzian metric manifolds and provides consistent backgrounds for physics. Extending standard mathematical constructions known from Finsler spaces, we show that geometric objects like the Cartan nonlinear connection and its curvature are well defined almost everywhere on Finsler spacetimes, including their null structure. This allows us to describe the complete causal structure in terms of timelike and null curves; these are essential to model physical observers and the propagation of light. We prove that the timelike directions form an open convex cone with a null boundary, as is the case in Lorentzian geometry. Moreover, we develop action integrals for physical field theories on Finsler spacetimes, and tools to deduce the corresponding equations of motion. These are applied to construct a theory of electrodynamics that confirms the claimed propagation of light along Finsler null geodesics.