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Sample records for aucune relation causale

  1. Quantum-coherent mixtures of causal relations

    PubMed Central

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

    2017-01-01

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

  2. Relating Granger causality to long-term causal effects.

    PubMed

    Smirnov, Dmitry A; Mokhov, Igor I

    2015-10-01

    In estimation of causal couplings between observed processes, it is important to characterize coupling roles at various time scales. The widely used Granger causality reflects short-term effects: it shows how strongly perturbations of a current state of one process affect near future states of another process, and it quantifies that via prediction improvement (PI) in autoregressive models. However, it is often more important to evaluate the effects of coupling on long-term statistics, e.g., to find out how strongly the presence of coupling changes the variance of a driven process as compared to an uncoupled case. No general relationships between Granger causality and such long-term effects are known. Here, we pose the problem of relating these two types of coupling characteristics, and we solve it for a class of stochastic systems. Namely, for overdamped linear oscillators, we rigorously derive that the above long-term effect is proportional to the short-term effects, with the proportionality coefficient depending on the prediction interval and relaxation times. We reveal that this coefficient is typically considerably greater than unity so that small normalized PI values may well correspond to quite large long-term effects of coupling. The applicability of the derived relationship to wider classes of systems, its limitations, and its value for further research are discussed. To give a real-world example, we analyze couplings between large-scale climatic processes related to sea surface temperature variations in equatorial Pacific and North Atlantic regions.

  3. Relating Granger causality to long-term causal effects

    NASA Astrophysics Data System (ADS)

    Smirnov, Dmitry A.; Mokhov, Igor I.

    2015-10-01

    In estimation of causal couplings between observed processes, it is important to characterize coupling roles at various time scales. The widely used Granger causality reflects short-term effects: it shows how strongly perturbations of a current state of one process affect near future states of another process, and it quantifies that via prediction improvement (PI) in autoregressive models. However, it is often more important to evaluate the effects of coupling on long-term statistics, e.g., to find out how strongly the presence of coupling changes the variance of a driven process as compared to an uncoupled case. No general relationships between Granger causality and such long-term effects are known. Here, we pose the problem of relating these two types of coupling characteristics, and we solve it for a class of stochastic systems. Namely, for overdamped linear oscillators, we rigorously derive that the above long-term effect is proportional to the short-term effects, with the proportionality coefficient depending on the prediction interval and relaxation times. We reveal that this coefficient is typically considerably greater than unity so that small normalized PI values may well correspond to quite large long-term effects of coupling. The applicability of the derived relationship to wider classes of systems, its limitations, and its value for further research are discussed. To give a real-world example, we analyze couplings between large-scale climatic processes related to sea surface temperature variations in equatorial Pacific and North Atlantic regions.

  4. Towards an Algebra for Analyzing Causal Relations.

    ERIC Educational Resources Information Center

    Ellett, Frederick S., Jr.; Ericson, David P.

    Correlation-based approaches to causal analysis contain too much irrelevant information that masks and modulates the true nature of causal processes in the world. Both causal modeling and path analysis/structural equations give the wrong answers for certain conceptions of causation, given certain assumptions about the "error" variables.…

  5. Are bruxism and the bite causally related?

    PubMed

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

    2012-07-01

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

  6. Causal Relations and Feature Similarity in Children's Inductive Reasoning

    ERIC Educational Resources Information Center

    Hayes, Brett K.; Thompson, Susan P.

    2007-01-01

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

  7. 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…

  8. 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…

  9. Causal relations and feature similarity in children's inductive reasoning.

    PubMed

    Hayes, Brett K; Thompson, Susan P

    2007-08-01

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

  10. Toddlers infer higher-order relational principles in causal learning.

    PubMed

    Walker, Caren M; Gopnik, Alison

    2014-01-01

    Children make inductive inferences about the causal properties of individual objects from a very young age. When can they infer higher-order relational properties? In three experiments, we examined 18- to 30-month-olds' relational inferences in a causal task. Results suggest that at this age, children are able to infer a higher-order relational causal principle from just a few observations and use this inference to guide their own subsequent actions and bring about a novel causal outcome. Moreover, the children passed a revised version of the relational match-to-sample task that has proven very difficult for nonhuman primates. The findings are considered in light of their implications for understanding the nature of relational and causal reasoning, and their evolutionary origins.

  11. 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…

  12. 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.

  13. 'Mendelian randomization': an approach for exploring causal relations in epidemiology.

    PubMed

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

    2017-04-01

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

  14. Multivariate Granger Causality Analysis of Obesity Related Variables.

    PubMed

    Mukhopadhyay, Nitai D; Wheeler, David; Sabo, Roy; Sun, Shumei S

    Obesity is a complex health outcome that is a combination of multiple health indicators. Here we attempt to explore the dependence network among multiple aspects of obesity. Two longitudinal cohort studies across multiple decades have been used. The concept of causality is defined similar to Granger causality among multiple time series, however, modified to accommodate multivariate time series as the nodes of the network. Our analysis reveals relatively central position of physical measurements and blood chemistry measures in the overall network across both genders. Also there are some patterns specific to only male or female population. The geometry of the causality network is expected to help in our strategy to control the increasing trend of obesity rate.

  15. 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

  16. 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…

  17. Causal relationships among factors associated with cancer-related fatigue.

    PubMed

    Seo, YoungMin; Oh, HyunSoo; Seo, WhaSook

    2010-12-01

    This study was conducted to develop and verify a comprehensive model, which illustrates the dynamic causal relationships between fatigue and its associated factors in cancer patients. The subjects were 110 in- or out-patients with various types of cancer being treated at a University Hospital, Incheon, South Korea. The comprehensive model consists of physical distress, sleep-related, physiologic, psychological distress, physical performance, and exercise factors. Psychological distress had a significant direct effect on physical distress, and 81% of the variance in physical distress was explained by psychological distress. While psychological distress showed to have a significant total effect (the sum of direct effects of psychological distress and indirect effects through its relationship with physical distress) on fatigue, it was not found to have a significant direct effect on fatigue. Only exercise had a significant direct effect on fatigue and 70% of fatigue variance was explained by exercise alone. All remaining factors were not found to have significant direct effects on fatigue. The hypothetical model was well suited to explain cancer-related fatigue. Our result indicates that psychological distress should be relieved in combination with a strategy to reduce physical distress in order to obtain better outcomes with respect to cancer-related fatigue. Only exercise had a significant direct effect on fatigue. In terms of the nursing implications, the proposed model can help oncology nurses better understand cancer-related fatigue and assess presence of correctable correlates. This model can be a future framework when developing intervention strategies for cancer-related fatigue. Copyright © 2009 Elsevier Ltd. All rights reserved.

  18. Geomagnetic field and climate: Causal relations with some atmospheric variables

    NASA Astrophysics Data System (ADS)

    Kilifarska, N. A.; Bakhmutov, V. G.; Mel'nik, G. V.

    2015-09-01

    The relationship between climatic parameters and the Earth's magnetic field has been reported by many authors. However, the absence of a feasible mechanism accounting for this relationship has impeded progress in this research field. Based on the instrumental observations, we reveal the spatiotemporal relationship between the key structures in the geomagnetic field, surface air temperature and pressure fields, ozone, and the specific humidity near the tropopause. As one of the probable explanations of these correlations, we suggest the following chain of the causal relations: (1) modulation of the intensity and penetration depth of energetic particles (galactic cosmic rays (GCRs)) in the Earth's atmosphere by the geomagnetic field; (2) the distortion of the ozone density near the tropopause under the action of GCRs; (3) the change in temperature near the tropopause due to the high absorbing capacity of ozone; (4) the adjustment of the extra-tropical upper tropospheric static stability and, consequently, specific humidity, to the modified tropopause temperature; and (5) the change in the surface air temperature due to the increase/decrease of the water vapor greenhouse effect.

  19. Perceived causal relations: novel methodology for assessing client attributions about causal associations between variables including symptoms and functional impairment.

    PubMed

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

    2012-12-01

    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 conceptualization, treatment, and psychological theory regarding the etiology of comorbid disorders. The present study developed and evaluated a novel psychological assessment methodology for measuring Perceived Causal Relations (PCR) and examined its psychometric properties as applied to the question of whether posttraumatic stress and anxiety symptoms represent causal risk factors for depressive symptoms in 225 undergraduates. Participants attributed their symptoms of anxiety and posttraumatic reexperiencing as significant causes of their depressive symptoms. Exploratory analyses identified a listing of symptoms reliably attributed as significant causes of other symptoms and functional impairment, as well as a listing of symptoms reliably attributed as significant effects (outcomes) of other symptoms and functional impairment. The PCR method has promise as an idiographic approach to assessing the causes and consequences of comorbid psychiatric symptoms and associated functional impairment. Research is required to assess the relevance and replicate these findings in distinct psychiatric groups experiencing various symptomatic presentations. Future research may also examine PCR ratings associating other individual differences, for example, between measures of history (e.g., life events), life choices, and personality.

  20. Puzzles in modern biology. III.Two kinds of causality in age-related disease

    PubMed Central

    Frank, Steven A.

    2017-01-01

    The two primary causal dimensions of age-related disease are rate and function. Change in rate of disease development shifts the age of onset. Change in physiological function provides necessary steps in disease progression. A causal factor may alter the rate of physiological change, but that causal factor itself may have no direct physiological role. Alternatively, a causal factor may provide a necessary physiological function, but that causal factor itself may not alter the rate of disease onset. The rate-function duality provides the basis for solving puzzles of age-related disease. Causal factors of cancer illustrate the duality between rate processes of discovery, such as somatic mutation, and necessary physiological functions, such as invasive penetration across tissue barriers. Examples from cancer suggest general principles of age-related disease. PMID:28184283

  1. Puzzles in modern biology. III.Two kinds of causality in age-related disease.

    PubMed

    Frank, Steven A

    2016-01-01

    The two primary causal dimensions of age-related disease are rate and function. Change in rate of disease development shifts the age of onset. Change in physiological function provides necessary steps in disease progression. A causal factor may alter the rate of physiological change, but that causal factor itself may have no direct physiological role. Alternatively, a causal factor may provide a necessary physiological function, but that causal factor itself may not alter the rate of disease onset. The rate-function duality provides the basis for solving puzzles of age-related disease. Causal factors of cancer illustrate the duality between rate processes of discovery, such as somatic mutation, and necessary physiological functions, such as invasive penetration across tissue barriers. Examples from cancer suggest general principles of age-related disease.

  2. Causal Inference and Language Comprehension: Event-Related Potential Investigations

    ERIC Educational Resources Information Center

    Davenport, Tristan S.

    2014-01-01

    The most important information conveyed by language is often contained not in the utterance itself, but in the interaction between the utterance and the comprehender's knowledge of the world and the current situation. This dissertation uses psycholinguistic methods to explore the effects of a common type of inference--causal inference--on language…

  3. Causal Inference and Language Comprehension: Event-Related Potential Investigations

    ERIC Educational Resources Information Center

    Davenport, Tristan S.

    2014-01-01

    The most important information conveyed by language is often contained not in the utterance itself, but in the interaction between the utterance and the comprehender's knowledge of the world and the current situation. This dissertation uses psycholinguistic methods to explore the effects of a common type of inference--causal inference--on language…

  4. 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.

  5. [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.

  6. Is There a Causal Relation between Mathematical Creativity and Mathematical Problem-Solving Performance?

    ERIC Educational Resources Information Center

    Tyagi, Tarun Kumar

    2016-01-01

    The relationship between mathematical creativity (MC) and mathematical problem-solving performance (MP) has often been studied but the causal relation between these two constructs has yet to be clearly reported. The main purpose of this study was to define the causal relationship between MC and MP. Data from a representative sample of 480…

  7. Children's conceptions of psychological causality as related to subjective responsibility, conservation, and language

    ERIC Educational Resources Information Center

    Whiteman, Martin

    1976-01-01

    The problems examined in this study were (1) age differences in grasp of psychological causality during middle childhood, (2) the exploration of possible mediating abilities of a physical-logical nature accounting for such age differences and (3) relations between cognition of psychological causality and intentionality in moral judgment.…

  8. Is There a Causal Relation between Mathematical Creativity and Mathematical Problem-Solving Performance?

    ERIC Educational Resources Information Center

    Tyagi, Tarun Kumar

    2016-01-01

    The relationship between mathematical creativity (MC) and mathematical problem-solving performance (MP) has often been studied but the causal relation between these two constructs has yet to be clearly reported. The main purpose of this study was to define the causal relationship between MC and MP. Data from a representative sample of 480…

  9. Processing of positive-causal and negative-causal coherence relations in primary school children and adults: a test of the cumulative cognitive complexity approach in German.

    PubMed

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

    2017-03-01

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

  10. Statistical approaches for enhancing causal interpretation of the M to Y relation in mediation analysis.

    PubMed

    MacKinnon, David P; Pirlott, Angela G

    2015-02-01

    Statistical mediation methods provide valuable information about underlying mediating psychological processes, but the ability to infer that the mediator variable causes the outcome variable is more complex than widely known. Researchers have recently emphasized how violating assumptions about confounder bias severely limits causal inference of the mediator to dependent variable relation. Our article describes and addresses these limitations by drawing on new statistical developments in causal mediation analysis. We first review the assumptions underlying causal inference and discuss three ways to examine the effects of confounder bias when assumptions are violated. We then describe four approaches to address the influence of confounding variables and enhance causal inference, including comprehensive structural equation models, instrumental variable methods, principal stratification, and inverse probability weighting. Our goal is to further the adoption of statistical methods to enhance causal inference in mediation studies.

  11. Statistical Approaches for Enhancing Causal Interpretation of the M to Y Relation in Mediation Analysis

    PubMed Central

    MacKinnon, David P.; Pirlott, Angela G.

    2016-01-01

    Statistical mediation methods provide valuable information about underlying mediating psychological processes, but the ability to infer that the mediator variable causes the outcome variable is more complex than widely known. Researchers have recently emphasized how violating assumptions about confounder bias severely limits causal inference of the mediator to dependent variable relation. Our article describes and addresses these limitations by drawing on new statistical developments in causal mediation analysis. We first review the assumptions underlying causal inference and discuss three ways to examine the effects of confounder bias when assumptions are violated. We then describe four approaches to address the influence of confounding variables and enhance causal inference, including comprehensive structural equation models, instrumental variable methods, principal stratification, and inverse probability weighting. Our goal is to further the adoption of statistical methods to enhance causal inference in mediation studies. PMID:25063043

  12. Transitive closure of subsumption and causal relations in a large ontology of radiological diagnosis.

    PubMed

    Kahn, Charles E

    2016-06-01

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

  13. 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.

  14. 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

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

    PubMed

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

    2015-01-01

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

  16. 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…

  17. 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…

  18. The development of reasoning about the temporal and causal relations among past, present, and future events.

    PubMed

    Lohse, Karoline; Kalitschke, Theresa; Ruthmann, Katja; Rakoczy, Hannes

    2015-10-01

    Children's capacity to reason about temporal and causal relations among past, present, and future events was investigated. In two studies, 4- and 6-year-olds (N=160) received structurally analogous search and planning tasks that required retrospective or prospective temporal-causal reasoning, respectively. The search task was compared with a closely matched control task that did not require temporal-causal reasoning. Results revealed that (a) both age groups solved the control task, (b) 6-year-olds mastered both retrospective and prospective tasks, and (c) 4-year-olds showed limited competence in both retrospective and prospective tasks. The current study, thus, suggests that flexible temporal-causal reasoning develops in parallel for past- and future-directed reasoning, is qualitatively different from simpler forms of temporal cognition, and develops during the late preschool years.

  19. 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…

  20. 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…

  1. 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

  2. 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.

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

    PubMed

    Yin, Yu; Yao, Dezhong

    2016-07-08

    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.

  4. 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

  5. Extracting causal relations on HIV drug resistance from literature

    PubMed Central

    2010-01-01

    Background In HIV treatment it is critical to have up-to-date resistance data of applicable drugs since HIV has a very high rate of mutation. These data are made available through scientific publications and must be extracted manually by experts in order to be used by virologists and medical doctors. Therefore there is an urgent need for a tool that partially automates this process and is able to retrieve relations between drugs and virus mutations from literature. Results In this work we present a novel method to extract and combine relationships between HIV drugs and mutations in viral genomes. Our extraction method is based on natural language processing (NLP) which produces grammatical relations and applies a set of rules to these relations. We applied our method to a relevant set of PubMed abstracts and obtained 2,434 extracted relations with an estimated performance of 84% for F-score. We then combined the extracted relations using logistic regression to generate resistance values for each pair. The results of this relation combination show more than 85% agreement with the Stanford HIVDB for the ten most frequently occurring mutations. The system is used in 5 hospitals from the Virolab project http://www.virolab.org to preselect the most relevant novel resistance data from literature and present those to virologists and medical doctors for further evaluation. Conclusions The proposed relation extraction and combination method has a good performance on extracting HIV drug resistance data. It can be used in large-scale relation extraction experiments. The developed methods can also be applied to extract other type of relations such as gene-protein, gene-disease, and disease-mutation. PMID:20178611

  6. 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…

  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. Commutative deformations of general relativity: nonlocality, causality, and dark matter

    NASA Astrophysics Data System (ADS)

    de Vegvar, P. G. N.

    2017-01-01

    Hopf algebra methods are applied to study Drinfeld twists of (3+1)-diffeomorphisms and deformed general relativity on commutative manifolds. A classical nonlocality length scale is produced above which microcausality emerges. Matter fields are utilized to generate self-consistent Abelian Drinfeld twists in a background independent manner and their continuous and discrete symmetries are examined. There is negligible experimental effect on the standard model of particles. While baryonic twist producing matter would begin to behave acausally for rest masses above {˜ }1-10 TeV, other possibilities are viable dark matter candidates or a right-handed neutrino. First order deformed Maxwell equations are derived and yield immeasurably small cosmological dispersion and produce a propagation horizon only for photons at or above Planck energies. This model incorporates dark matter without any appeal to extra dimensions, supersymmetry, strings, grand unified theories, mirror worlds, or modifications of Newtonian dynamics.

  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. [Zinc and epilepsy: is there a causal relation between them?].

    PubMed

    Moreno, C B; Gutiérrez-Alvarez, A M; González-Reyes, R E

    Zinc is a fundamental trace element for an adequate nervous system function. It has been suggested that in the brain, a zinc homeostasis alteration may be associated with the genesis of epilepsy, although it is not yet determined if concentrations of zinc are a cause or a consequence of seizures. Another poorly studied aspect is the relationship between antiepileptic drugs and the neuronal zinc behaviour. We perform a systematic review of the literature to evaluate the role that zinc plays in epilepsy as well as the antiepileptic effect of zinc concentrations. Databases such as MEDLINE, EMBASE, SCISEARCH and LILACS were consulted from January 1974 to July 2005. All articles published in English and Spanish were considered. A manual review of the references present in each article was done in order to identify the articles that the electronic search may have not found itself. The title and abstract of the potential articles were analyzed before asking for the complete article. However, articles that seemed ambiguous were completely analyzed later to establish their relevance. Clinical research in epilepsy presented contradictory results. In fact, the reviewed studies, both animal and human, did not give enough evidence to determine if organic zinc variations are directly related to epilepsy. Most of them gave not statistically significant results.

  11. Confirmatory Analytic Tests of Three Causal Models Relating Job Perceptions to Job Satisfaction.

    DTIC Science & Technology

    1984-12-01

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

  12. Relations between causal attributions for stuttering and psychological well-being in adults who stutter.

    PubMed

    Boyle, Michael

    2016-02-01

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

  13. Relations among Parental Causal Attributions and Children's Math Performance and Task Persistence

    ERIC Educational Resources Information Center

    Tõeväli, Paula-Karoliina; Kikas, Eve

    2017-01-01

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

  14. 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

  15. 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.

  16. Do capuchin monkeys (Cebus apella) diagnose causal relations in the absence of a direct reward?

    PubMed

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

    2014-01-01

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

  17. Do Capuchin Monkeys (Cebus apella) Diagnose Causal Relations in the Absence of a Direct Reward?

    PubMed Central

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

    2014-01-01

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

  18. 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.

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

    PubMed Central

    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 (200-500ms) elicited by unrelated related words was more negative than all related words. Furthermore, the late frontal distributed negativity (500-700ms) 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

  20. 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

  1. 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

  2. Sex differences in the inference and perception of causal relations within a video game.

    PubMed

    Young, Michael E

    2014-01-01

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

  3. 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.

  4. Causal Beliefs and Effects upon Mental Illness Identification Among Chinese Immigrant Relatives of Individuals with Psychosis

    PubMed Central

    Wonpat-Borja, Ahtoy J.

    2013-01-01

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

  5. Causality patterns and machine learning for the extraction of problem-action relations in discharge summaries.

    PubMed

    Seol, Jae-Wook; Yi, Wangjin; Choi, Jinwook; Lee, Kyung Soon

    2017-02-01

    Clinical narrative text includes information related to a patient's medical history such as chronological progression of medical problems and clinical treatments. A chronological view of a patient's history makes clinical audits easier and improves quality of care. In this paper, we propose a clinical Problem-Action relation extraction method, based on clinical semantic units and event causality patterns, to present a chronological view of a patient's problem and a doctor's action. Based on our observation that a clinical text describes a patient's medical problems and a doctor's treatments in chronological order, a clinical semantic unit is defined as a problem and/or an action relation. Since a clinical event is a basic unit of the problem and action relation, events are extracted from narrative texts, based on the external knowledge resources context features of the conditional random fields. A clinical semantic unit is extracted from each sentence based on time expressions and context structures of events. Then, a clinical semantic unit is classified into a problem and/or action relation based on the event causality patterns of the support vector machines. Experimental results on Korean discharge summaries show 78.8% performance in the F1-measure. This result shows that the proposed method is effectively classifies clinical Problem-Action relations.

  6. 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

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

    PubMed

    Cox, Louis Anthony Tony

    2017-08-01

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

  8. [Severe bronchial stenosis with upstream bronchiectasis in an arc welder: causal relation or epiphenomenon?].

    PubMed

    Charpin, D; Jullian, H; Garbe, L; Cau, P; Fuentes, P; Vervloet, D

    1997-04-01

    This case concerns an arc welder who presented suppurative bronchiectasis and episodes of purulent left side pleurisy in relation to cystic bronchiectasis of the left lower lobe and a very severe stenosis at the origin of the main left bronchus. The medicolegal problem was to assess the causal relationship between these lesions and occupational exposure. They do not come under the heading of table 44 of the General List and we made this the aim of discretionary award in front of a regional committee of compensation for occupational disease.

  9. 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

  10. Relativistic causality

    NASA Astrophysics Data System (ADS)

    Valente, Giovanni; Owen Weatherall, James

    2014-11-01

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

  11. Genetic Evidence for Causal Relationships Between Maternal Obesity-Related Traits and Birth Weight.

    PubMed

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

    2016-03-15

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

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

    PubMed

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

    2016-10-01

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

  13. Children's developing understanding of the relation between variable causal efficacy and mechanistic complexity.

    PubMed

    Erb, Christopher D; Buchanan, David W; Sobel, David M

    2013-12-01

    Two experiments investigated 3-4-year-olds' ability to infer the causal mechanisms for a pair of lights. In both experiments the exterior of the two lights appeared identical. In Experiment 1, one light displayed a stable activation pattern of a single color while the other light displayed a variable pattern of activation by cycling through a series of different colors (i.e., a more varied effect). Children were asked to judge which light had a more complex internal structure. Four-year-olds were more likely to match the light with the more variable effect with a more complex internal mechanism and the light with the more stable effect with a less complex mechanism. Three-year-olds' responses were at chance. Experiment 2 replicated this finding when the activation patterns of the two lights were described verbally but never demonstrated. Taken together, these results suggest that 4-year-olds appreciate that the variability of an object's causal efficacy is related to the complexity of its internal mechanistic structure. Copyright © 2013 Elsevier B.V. All rights reserved.

  14. 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.

  15. Direct and indirect causal effects of heterozygosity on fitness-related traits in Alpine ibex.

    PubMed

    Brambilla, Alice; Biebach, Iris; Bassano, Bruno; Bogliani, Giuseppe; von Hardenberg, Achaz

    2015-01-07

    Heterozygosity-fitness correlations (HFCs) are a useful tool to investigate the effects of inbreeding in wild populations, but are not informative in distinguishing between direct and indirect effects of heterozygosity on fitness-related traits. We tested HFCs in male Alpine ibex (Capra ibex) in a free-ranging population (which suffered a severe bottleneck at the end of the eighteenth century) and used confirmatory path analysis to disentangle the causal relationships between heterozygosity and fitness-related traits. We tested HFCs in 149 male individuals born between 1985 and 2009. We found that standardized multi-locus heterozygosity (MLH), calculated from 37 microsatellite loci, was related to body mass and horn growth, which are known to be important fitness-related traits, and to faecal egg counts (FECs) of nematode eggs, a proxy of parasite resistance. Then, using confirmatory path analysis, we were able to show that the effect of MLH on horn growth was not direct but mediated by body mass and FEC. HFCs do not necessarily imply direct genetic effects on fitness-related traits, which instead can be mediated by other traits in complex and unexpected ways.

  16. Identification of causal relationships among traits related to drought resistance in Stylosanthes scabra using QTL analysis.

    PubMed

    Thumma, B R; Naidu, B P; Chandra, A; Cameron, D F; Bahnisch, L M; Liu, C

    2001-02-01

    Previous studies have shown that a negative relationship exists between transpiration efficiency (TE) and carbon isotope discrimination (Delta) and between TE and specific leaf area (SLA) in Stylosanthes scabra. A glasshouse experiment was conducted to confirm these relationships in an F(2) population and to study the causal nature of these relationships through quantitative trait loci (QTL) analysis. One hundred and twenty F(2) genotypes from a cross between two genotypes within S. scabra were used. Three replications for each genotype were maintained through vegetative propagation. Water stress was imposed by maintaining plants at 40% of field capacity for about 45 d. To facilitate QTL analysis, a genetic linkage map consisting of 151 RAPD markers was developed. Results from this study show that Delta was significantly and negatively correlated with TE and biomass production. Similarly, SLA showed significant negative correlation with TE and biomass production. Most of the QTL for TE and Delta were present on linkage groups 5 and 11. Similarly, QTL for SLA, transpiration and biomass productivity traits were clustered on linkage groups 13 and 24. One unlinked marker was also associated with these traits. There were several markers coincident between different traits. At all the coincident QTL, the direction of QTL effects was consistent with phenotypic data. At the coincident markers between TE and Delta, high alleles of TE were associated with low alleles of Delta. Similarly, low alleles of SLA were associated with high alleles of biomass productivity traits and transpiration. At the coincident markers between trans-4-hydroxy-N:-methyl proline (MHP) and relative water content (RWC), low alleles of MHP were associated with high alleles of RWC. This study suggests the causal nature of the relationship between TE and Delta. Phenotypic data and QTL data show that SLA was more closely associated with biomass production than with TE. This study also shows that a cause

  17. The Temporal Order of Word Presentation Modulates the Amplitudes of P2 and N400 during Recognition of Causal Relations

    PubMed Central

    Liang, Xiuling; Xiao, Feng; Wu, Lijun; Chen, Qingfei; Lei, Yi; Li, Hong

    2016-01-01

    The processing of causal relations has been constantly found to be asymmetrical once the roles of cause and effect are assigned to objects in interactions. We used a relationship recognition paradigm and recorded electroencephalographic (EEG) signals to explore the neural mechanism underlying the asymmetrical representations of causal relations in semantic memory. The results revealed that the verification of causal relations is faster if two words appear in “cause-effect” order (e.g., virus-epidemic) than if they appear in “effect-cause” order (e.g., epidemic-virus), whereas no such asymmetrical representation was found for the verification of hierarchical relations with reverse orders (e.g., bird-sparrow vs. sparrow-bird) in Experiment 1. Furthermore, the P2 amplitude elicited by “superordinate-subordinate” order was larger than that when in reverse order, whereas the N400 effect elicited by “cause-effect” order was smaller (more positive) than when in reverse order. However, no such asymmetry, as well as P2 and N400 components, were observed when verifying the existence of a general associative relation in Experiment 2. We suggested that the smaller N400 in cause-effect order indicates their increased salience in semantic memory relative to the effect-cause order. These results provide evidence for dissociable neural processes, which are related to role binding, contributing to the generation of causal asymmetry. PMID:27994564

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

    PubMed

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

    2015-04-01

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

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2015-10-20

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

  1. 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.

  2. Causality in the Association between P300 and Alpha Event-Related Desynchronization

    PubMed Central

    Zhang, Zhiguo; Hu, Yong

    2012-01-01

    Recent findings indicated that both P300 and alpha event-related desynchronization (α-ERD) were associated, and similarly involved in cognitive brain functioning, e.g., attention allocation and memory updating. However, an explicit causal influence between the neural generators of P300 and α-ERD has not yet been investigated. In the present study, using an oddball task paradigm, we assessed the task effect (target vs. non-target) on P300 and α-ERD elicited by stimuli of four sensory modalities, i.e., audition, vision, somatosensory, and pain, estimated their respective neural generators, and investigated the information flow among their neural generators using time-varying effective connectivity in the target condition. Across sensory modalities, the scalp topographies of P300 and α-ERD were similar and respectively maximal at parietal and occipital regions in the target condition. Source analysis revealed that P300 and α-ERD were mainly generated from posterior cingulate cortex and occipital lobe respectively. As revealed by time-varying effective connectivity, the cortical information was consistently flowed from α-ERD sources to P300 sources in the target condition for all four sensory modalities. All these findings showed that P300 in the target condition is modulated by the changes of α-ERD, which would be useful to explore neural mechanism of cognitive information processing in the human brain. PMID:22511933

  3. The Causal Effects of Relational Security and Insecurity on Condom Use Attitudes and Acquisition Behavior.

    PubMed

    Sakaluk, John Kitchener; Gillath, Omri

    2016-02-01

    Research on attachment and condom use has been limited to correlational studies of self-report measures, yielding inconsistent results. Here, we examined the causal effects of attachment priming on self-reported condom use attitudes and an observational measure of condom acquisition behavior. In three experiments, participants were exposed to one of three attachment primes (security, anxiety, or avoidance) or a control prime. For Study 1, participants in the security and anxiety conditions preferred condom non-use to a greater extent, compared to participants in the avoidance condition. This effect was replicated in Study 2, and was mediated by perceptions of sexual health threat. In Study 3, the effect of security priming on condom acquisition behavior was eliminated through the use of a framing manipulation, though the effect of primed attachment on condom use attitudes was not significant. A meta-analysis, however, revealed that the predicted effects of attachment priming were consistent across the three studies, supporting the role of attachment in evaluations of condom use. Priming attachment security or anxiety leads participants to perceive their sexual partners as less of a sexual health threat, resulting in a devaluation of condom use. Primed security also reduced condom acquisition behavior, though this negative effect eliminated by framing condoms as protecting a partner's sexual health. Overall, these studies suggest that relational factors, such as attachment, require greater consideration when studying sexual health and designing interventions.

  4. [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.

  5. Why the assessment of causality in brain-behavior relations requires brain stimulation.

    PubMed

    Silvanto, Juha; Pascual-Leone, Alvaro

    2012-04-01

    A central aim in cognitive neuroscience is to explain how neural activity gives rise to perception and behavior; the causal link of paramount interest is thus from brain to behavior. Functional neuroimaging studies, however, tend to provide information in the opposite direction by informing us how manipulation of behavior may affect neural activity. Although this may provide valuable insights into neuronal properties, one cannot use such evidence to make inferences about the behavioral significance of the observed activations; if A causes B, it does not necessarily follow that B causes A. In contrast, brain stimulation techniques enable us to directly modulate brain activity as the source of behavior and thus establish causal links.

  6. Is young fatherhood causally related to midlife mortality? A sibling fixed-effect study in Finland.

    PubMed

    Einiö, Elina; Nisén, Jessica; Martikainen, Pekka

    2015-11-01

    Previous studies have shown that young fatherhood is associated with higher later-life mortality. It is unclear whether the association is credible, in the sense that mortality and young fatherhood appear to be associated because both are determined by family-related environmental, socioeconomic and genetic characteristics. We used a household-based 10% sample drawn from the 1950 Finnish census to estimate all-cause mortality of fathers born during 1940-1950. The fathers were followed from age 45 until death, or the end of age 54. We used a standard Cox model and a sibling fixed-effects Cox model to examine whether the effect of young fatherhood was independent of observed adulthood characteristics and unobserved early-life characteristics shared by brothers. Men who had their first child before the age of 22 or at ages 22-24 had higher mortality as compared with their brothers who had their first child at the median or mean age of 25-26. Men who had their first child later at ages 30-44 had lower mortality than their brothers who had a first child before the age of 25. The pattern of results from a standard model was similar to that obtained from a fixed-effects sibling model. The findings suggest a causal effect of young fatherhood on mortality and highlight the need to support young fathers in their family life to improve health behaviours and health. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  7. 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

  8. Deciphering causal and statistical relations of molecular aberrations and gene expressions in NCI-60 cell lines

    PubMed Central

    2011-01-01

    Background Cancer cells harbor a large number of molecular alterations such as mutations, amplifications and deletions on DNA sequences and epigenetic changes on DNA methylations. These aberrations may dysregulate gene expressions, which in turn drive the malignancy of tumors. Deciphering the causal and statistical relations of molecular aberrations and gene expressions is critical for understanding the molecular mechanisms of clinical phenotypes. Results In this work, we proposed a computational method to reconstruct association modules containing driver aberrations, passenger mRNA or microRNA expressions, and putative regulators that mediate the effects from drivers to passengers. By applying the module-finding algorithm to the integrated datasets of NCI-60 cancer cell lines, we found that gene expressions were driven by diverse molecular aberrations including chromosomal segments' copy number variations, gene mutations and DNA methylations, microRNA expressions, and the expressions of transcription factors. In-silico validation indicated that passenger genes were enriched with the regulator binding motifs, functional categories or pathways where the drivers were involved, and co-citations with the driver/regulator genes. Moreover, 6 of 11 predicted MYB targets were down-regulated in an MYB-siRNA treated leukemia cell line. In addition, microRNA expressions were driven by distinct mechanisms from mRNA expressions. Conclusions The results provide rich mechanistic information regarding molecular aberrations and gene expressions in cancer genomes. This kind of integrative analysis will become an important tool for the diagnosis and treatment of cancer in the era of personalized medicine. PMID:22051105

  9. Trauma-Related Altered States of Consciousness (TRASC) and Functional Impairment II: Perceived Causal Relationships in an Online Sample.

    PubMed

    Tzannidakis, Nicole C A; Frewen, Paul

    2015-01-01

    Research supports the existence of a dissociative subtype of posttraumatic stress disorder, although studies have not directly compared the perceived impact of dissociative versus nondissociative posttraumatic symptoms on social and occupational functioning. In addition, research is beginning to differentiate between posttraumatic distress associated with normal waking consciousness (NWC) and dissociative experiences of trauma-related altered states of consciousness (TRASC) along multiple phenomenological dimensions. The current study investigated perceived causal relationships between posttraumatic symptoms associated with NWC-distress and TRASC on the one hand and interpersonal and occupational functioning on the other. Although both TRASC and NWC-distress independently accounted for variance in self-reported interpersonal and occupational problems, perceived causal relationship results showed that individuals tended to attribute their social and work-related problems more strongly to NWC-distress than to TRASC. Future research directions are discussed.

  10. Genetic insights into age-related macular degeneration: controversies addressing risk, causality, and therapeutics.

    PubMed

    Gorin, Michael B

    2012-08-01

    Age-related macular degeneration (AMD) is a common condition among the elderly population that leads to the progressive central vision loss and serious compromise of quality of life for its sufferers. It is also one of the few disorders for whom the investigation of its genetics has yielded rich insights into its diversity and causality and holds the promise of enabling clinicians to provide better risk assessments for individuals as well as to develop and selectively deploy new therapeutics to either prevent or slow the development of disease and lessen the threat of vision loss. The genetics of AMD began initially with the appreciation of familial aggregation and increase risk and expanded with the initial association of APOE variants with the disease. The first major breakthroughs came with family-based linkage studies of affected (and discordant) sibs, which identified a number of genetic loci and led to the targeted search of the 1q31 and 10q26 loci for associated variants. Three of the initial four reports for the CFH variant, Y402H, were based on regional candidate searches, as were the two initial reports of the ARMS2/HTRA1 locus variants. Case-control association studies initially also played a role in discovering the major genetic variants for AMD, and the success of those early studies have been used to fuel enthusiasm for the methodology for a number of diseases. Until 2010, all of the subsequent genetic variants associated with AMD came from candidate gene testing based on the complement factor pathway. In 2010, several large-scale genome-wide association studies (GWAS) identified genes that had not been previously identified. Much of this historical information is available in a number of recent reviews (Chen et al., 2010b; Deangelis et al., 2011; Fafowora and Gorin, 2012b; Francis and Klein, 2011; Kokotas et al., 2011). Large meta analysis of AMD GWAS has added new loci and variants to this collection (Chen et al., 2010a; Kopplin et al., 2010; Yu et

  11. Genetic insights into age-related macular degeneration: Controversies addressing Risk, Causality, and Therapeutics

    PubMed Central

    Gorin, Michael B.

    2012-01-01

    Age-related macular degeneration (AMD) is a common condition among the elderly population that leads to the progressive central vision loss and serious compromise of quality of life for its sufferers. It is also one of the few disorders for whom the investigation of its genetics has yielded rich insights into its diversity and causality and holds the promise of enabling clinicians to provide better risk assessments for individuals as well as to develop and selectively deploy new therapeutics to either prevent or slow the development of disease and lessen the threat of vision loss. The genetics of AMD began initially with the appreciation of familial aggregation and increase risk and expanded with the initial association of APOE variants with the disease. The first major breakthroughs came with family-based linkage studies of affected (and discordant) sibs, which identified a number of genetic loci and led to the targeted search of the 1q31 and 10q26 loci for associated variants. Three of the initial four reports for the CFH variant, Y402H, were based on regional candidate searches, as were the two initial reports of the ARMS2/HTRA1 locus variants. Case-control association studies initially also played a role in discovering the major genetic variants for AMD, and the success of those early studies have been used to fuel enthusiasm for the methodology for a number of diseases. Until 2010, all of the subsequent genetic variants associated with AMD came from candidate gene testing based on the complement factor pathway. In 2010, several large-scale genome-wide association studies (GWAS) identified genes that had not been previously identified. Much of this historical information is available in a number of recent reviews.(Chen et al., 2010b; Deangelis et al., 2011; Fafowora and Gorin, 2012b; Francis and Klein, 2011; Kokotas et al., 2011) Large meta analysis of AMD GWAS has added new loci and variants to this collection.(Chen et al., 2010a; Kopplin et al., 2010; Yu et

  12. 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…

  13. 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…

  14. 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.

  15. Causality in thought.

    PubMed

    Sloman, Steven A; Lagnado, David

    2015-01-03

    Causal knowledge plays a crucial role in human thought, but the nature of causal representation and inference remains a puzzle. Can human causal inference be captured by relations of probabilistic dependency, or does it draw on richer forms of representation? This article explores this question by reviewing research in reasoning, decision making, various forms of judgment, and attribution. We endorse causal Bayesian networks as the best normative framework and as a productive guide to theory building. However, it is incomplete as an account of causal thinking. On the basis of a range of experimental work, we identify three hallmarks of causal reasoning-the role of mechanism, narrative, and mental simulation-all of which go beyond mere probabilistic knowledge. We propose that the hallmarks are closely related. Mental simulations are representations over time of mechanisms. When multiple actors are involved, these simulations are aggregated into narratives.

  16. The Impact of Relative Poverty on Norwegian Adolescents’ Subjective Health: A Causal Analysis with Propensity Score Matching

    PubMed Central

    Elstad, Jon Ivar; Pedersen, Axel West

    2012-01-01

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

  17. The impact of relative poverty on Norwegian adolescents’ subjective health: a causal analysis with propensity score matching.

    PubMed

    Elstad, Jon Ivar; Pedersen, Axel West

    2012-12-18

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

  18. Sexual Harassment, Psychological Distress, and Problematic Drinking Behavior Among College Students: An Examination of Reciprocal Causal Relations.

    PubMed

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

    2017-01-01

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

  19. Causal Learning Mechanisms in Very Young Children: Two-, Three-, and Four-Year-Olds Infer Causal Relations from Patterns of Variation and Covariation.

    ERIC Educational Resources Information Center

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

    2001-01-01

    Investigated in 3 studies whether 2- to 4-year-olds make accurate causal inferences on the basis of patterns of variation and covariation. Found that all three age groups considered information from various patterns of variation and covariation in judgments regarding two objects and activation of a machine. Three- and 4-year-olds used the…

  20. 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

  1. Is High-Density Lipoprotein Cholesterol Causally Related to Kidney Function?

    PubMed Central

    Coassin, Stefan; Friedel, Salome; Köttgen, Anna

    2016-01-01

    Objective— A recent observational study with almost 2 million men reported an association between low high-density lipoprotein (HDL) cholesterol and worse kidney function. The causality of this association would be strongly supported if genetic variants associated with HDL cholesterol were also associated with kidney function. Approach and Results— We used 68 genetic variants (single-nucleotide polymorphisms [SNPs]) associated with HDL cholesterol in genome-wide association studies including >188 000 subjects and tested their association with estimated glomerular filtration rate (eGFR) using summary statistics from another genome-wide association studies meta-analysis of kidney function including ≤133 413 subjects. Fourteen of the 68 SNPs (21%) had a P value <0.05 compared with the 5% expected by chance (Binomial test P=5.8×10−6). After Bonferroni correction, 6 SNPs were still significantly associated with eGFR. The genetic variants with the strongest associations with HDL cholesterol concentrations were not the same as those with the strongest association with kidney function and vice versa. An evaluation of pleiotropy indicated that the effects of the HDL-associated SNPs on eGFR were not mediated by HDL cholesterol. In addition, we performed a Mendelian randomization analysis. This analysis revealed a positive but nonsignificant causal effect of HDL cholesterol–increasing variants on eGFR. Conclusions— In summary, our findings indicate that HDL cholesterol does not causally influence eGFR and propose pleiotropic effects on eGFR for some HDL cholesterol–associated SNPs. This may cause the observed association by mechanisms other than the mere HDL cholesterol concentration. PMID:27687604

  2. A Statistical Approach to Fine Mapping for the Identification of Potential Causal Variants Related to Bone Mineral Density.

    PubMed

    Greenbaum, Jonathan; Deng, Hong-Wen

    2017-08-01

    Although genomewide association studies (GWASs) have been able to successfully identify dozens of genetic loci associated with bone mineral density (BMD) and osteoporosis-related traits, very few of these loci have been confirmed to be causal. This is because in a given genetic region there may exist many trait-associated SNPs that are highly correlated. Although this correlation is useful for discovering novel associations, the high degree of linkage disequilibrium that persists throughout the genome presents a major challenge to discern which among these correlated variants has a direct effect on the trait. In this study we apply a recently developed Bayesian fine-mapping method, PAINTOR, to determine the SNPs that have the highest probability of causality for femoral neck (FNK) BMD and lumbar spine (LS) BMD. The advantage of this method is that it allows for the incorporation of information about GWAS summary statistics, linkage disequilibrium, and functional annotations to calculate a posterior probability of causality for SNPs across all loci of interest. We present a list of the top 10 candidate SNPs for each BMD trait to be followed up in future functional validation experiments. The SNPs rs2566752 (WLS) and rs436792 (ZNF621 and CTNNB1) are particularly noteworthy because they have more than 90% probability to be causal for both FNK and LS BMD. Using this statistical fine-mapping approach we expect to gain a better understanding of the genetic determinants contributing to BMD at multiple skeletal sites. © 2017 American Society for Bone and Mineral Research. © 2017 American Society for Bone and Mineral Research.

  3. The development of causal categorization.

    PubMed

    Hayes, Brett K; Rehder, Bob

    2012-08-01

    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. Classification patterns and logistic regression were used to diagnose the presence of independent effects of causal coherence, causal status, and relational centrality. Adult classification was driven primarily by coherence when causal links were deterministic (Experiment 1) but showed additional influences of causal status when links were probabilistic (Experiment 2). Children's classification was based primarily on causal coherence in both cases. There was no effect of relational centrality in either age group. These results suggest that the generative model (Rehder, 2003a) provides a good account of causal categorization in children as well as adults. Copyright © 2012 Cognitive Science Society, Inc.

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

    PubMed

    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.

  5. 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

  6. On the relation between the probabilistic characterization of the common cause and Bell's notion of local causality

    NASA Astrophysics Data System (ADS)

    Hofer-Szabó, Gábor

    2015-02-01

    In this paper the relation between the standard probabilistic characterization of the common cause (used for the derivation of the Bell inequalities) and Bell's notion of local causality will be investigated in the isotone net framework borrowed from algebraic quantum field theory. The logical role of two components in Bell's definition will be scrutinized; namely that the common cause is localized in the intersection of the past of the correlated events; and that it provides a complete specification of the 'beables' of this intersection.

  7. The development of causal reasoning.

    PubMed

    Kuhn, Deanna

    2012-05-01

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

  8. 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

  9. 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.

  10. Decay of Activity Complexes, Formation of Unipolar Magnetic Regions, and Coronal Holes in Their Causal Relation

    NASA Astrophysics Data System (ADS)

    Golubeva, E. M.; Mordvinov, A. V.

    2016-12-01

    The peculiar development of solar activity in the current cycle resulted in an asynchronous reversal of the Sun's polar fields. The asymmetry is also observed in the formation of polar coronal holes. A stable coronal hole was first formed at the South Pole, despite the later polar-field reversal there. The aim of this study is to understand the processes making this situation possible. Synoptic magnetic maps from the Global Oscillation Network Group and corresponding coronal-hole maps from the Extreme ultraviolet Imaging Telescope onboard the Solar and Heliospheric Observatory and the Atmospheric Imaging Assembly onboard the Solar Dynamics Observatory are analyzed here to study the causal relationship between the decay of activity complexes, evolution of large-scale magnetic fields, and formation of coronal holes. Ensembles of coronal holes associated with decaying active regions and activity complexes are presented. These ensembles take part in global rearrangements of the Sun's open magnetic flux. In particular, the south polar coronal hole was formed from an ensemble of coronal holes that came into existence after the decay of multiple activity complexes observed during 2014.

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

    PubMed

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

    2014-06-01

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

  12. 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

  13. 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.

  14. Response Theory for Equilibrium and Non-Equilibrium Statistical Mechanics: Causality and Generalized Kramers-Kronig Relations

    NASA Astrophysics Data System (ADS)

    Lucarini, Valerio

    2008-05-01

    We consider the general response theory recently proposed by Ruelle for describing the impact of small perturbations to the non-equilibrium steady states resulting from Axiom A dynamical systems. We show that the causality of the response functions entails the possibility of writing a set of Kramers-Kronig (K-K) relations for the corresponding susceptibilities at all orders of nonlinearity. Nonetheless, only a special class of directly observable susceptibilities obey K-K relations. Specific results are provided for the case of arbitrary order harmonic response, which allows for a very comprehensive K-K analysis and the establishment of sum rules connecting the asymptotic behavior of the harmonic generation susceptibility to the short-time response of the perturbed system. These results set in a more general theoretical framework previous findings obtained for optical systems and simple mechanical models, and shed light on the very general impact of considering the principle of causality for testing self-consistency: the described dispersion relations constitute unavoidable benchmarks that any experimental and model generated dataset must obey. The theory exposed in the present paper is dual to the time-dependent theory of perturbations to equilibrium states and to non-equilibrium steady states, and has in principle similar range of applicability and limitations. In order to connect the equilibrium and the non equilibrium steady state case, we show how to rewrite the classical response theory by Kubo so that response functions formally identical to those proposed by Ruelle, apart from the measure involved in the phase space integration, are obtained. These results, taking into account the chaotic hypothesis by Gallavotti and Cohen, might be relevant in several fields, including climate research. In particular, whereas the fluctuation-dissipation theorem does not work for non-equilibrium systems, because of the non-equivalence between internal and external

  15. 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

  16. Explicit causal relations between material damping ratio and phase velocity from exact solutions of the dispersion equations of linear viscoelasticity

    NASA Astrophysics Data System (ADS)

    Meza-Fajardo, Kristel C.; Lai, Carlo G.

    2007-12-01

    The theory of linear viscoelasticity is the simplest constitutive model that can be adopted to accurately predict the small-strain mechanical response of materials exhibiting the ability to both store and dissipate strain energy. An important result implied by this theory is the relationship existing between material attenuation and the velocity of propagation of a mechanical disturbance. The functional dependence of these important parameters is represented by the Kramers-Kronig (KK) equations, also known as dispersion equations, which are nothing but a statement of the necessary and sufficient conditions to satisfy physical causality. This paper illustrates the derivation of exact solutions of the KK equations to provide explicit relations between frequency-dependent phase velocity and material damping ratio (or equivalently, quality factor). The assumptions that form the basis of the derivation are not beyond those established by the standard theory of viscoelasticity for a viscoelastic solid. The explicit expression for phase velocity as a function of damping ratio was derived by means of the theory of linear singular integral equations, and in particular by the solution of the associated Homogeneous Riemann Boundary Value Problem. It is shown that the same solution may be obtained also by using the implications of physical causality on the Fourier Transform. On the other hand, the explicit solution for damping ratio as a function of phase velocity was found through the components of the complex wavenumber. The exact solutions make it possible to obtain frequency-dependent material damping ratio solely from phase velocity measurements, and conversely. Hence, these relations provide an innovative and inexpensive tool to determine the small-strain dynamic properties of geomaterials. It is shown that the obtained rigorous solutions are in good agreement with well-known solutions based on simplifying assumptions that have been developed in the fields of seismology

  17. 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.

  18. Public Relations Roles and Systems Theory: Functional and Historicist Causal Models.

    ERIC Educational Resources Information Center

    Broom, Glen M.

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

  19. Does a Causal Relation Exist between the Functional Hemispheric Asymmetries of Visual Processing Subsystems?

    ERIC Educational Resources Information Center

    Andresen, David R.; Marsolek, Chad J.

    2005-01-01

    Past research indicates that specific shape recognition and spatial-relations encoding rely on subsystems that exhibit right-hemisphere advantages, whereas abstract shape recognition and spatial-relations encoding rely on subsystems that exhibit left-hemisphere advantages. Given these apparent regularities, we tested whether asymmetries in shape…

  20. 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

  1. PPInterFinder—a mining tool for extracting causal relations on human proteins from literature

    PubMed Central

    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

  2. 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/

  3. Stuttering in relation to anxiety, temperament, and personality: review and analysis with focus on causality.

    PubMed

    Alm, Per A

    2014-06-01

    Anxiety and emotional reactions have a central role in many theories of stuttering, for example that persons who stutter would tend to have an emotionally sensitive temperament. The possible relation between stuttering and certain traits of temperament or personality were reviewed and analyzed, with focus on temporal relations (i.e., what comes first). It was consistently found that preschool children who stutter (as a group) do not show any tendencies toward elevated temperamental traits of shyness or social anxiety compared with children who do not stutter. Significant group differences were, however, repeatedly reported for traits associated with inattention and hyperactivity/impulsivity, which is likely to reflect a subgroup of children who stutter. Available data is not consistent with the proposal that the risk for persistent stuttering is increased by an emotionally reactive temperament in children who stutter. Speech-related social anxiety develops in many cases of stuttering, before adulthood. Reduction of social anxiety in adults who stutter does not in itself appear to result in significant improvement of speech fluency. Studies have not revealed any relation between the severity of the motor symptoms of stuttering and temperamental traits. It is proposed that situational variability of stuttering, related to social complexity, is an effect of interference from social cognition and not directly from the emotions of social anxiety. In summary, the studies in this review provide strong evidence that persons who stutter are not characterized by constitutional traits of anxiety or similar constructs. This paper provides a review and analysis of studies of anxiety, temperament, and personality, organized with the objective to clarify cause and effect relations. Readers will be able to (a) understand the importance of effect size and distribution of data for interpretation of group differences; (b) understand the role of temporal relations for interpretation

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

    PubMed Central

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

    2011-01-01

    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 (> 60 Hz) 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

  5. 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

  6. Deep Vein Thrombosis and True Crural Aneurysm: Misdiagnosis or Causal Relation?

    PubMed

    Floros, Nikolaos; Antoniou, Zoi; Papadakis, Marios

    2016-04-01

    True crural artery aneurysm is a rare clinical entity. Crural artery aneurysms are most frequently seen in men in their sixth decade without major cardiopulmonary diseases and are often associated with injury, superinfection, or vasculitis. We report the case of a 44-year-old man with a history of idiopathic deep vein thrombosis (DVT) as the first manifestation of a true crural artery aneurysm. To our knowledge, DVT is very rarely related with true crural artery aneurysms, with only 3 cases reported in the current literature. Open surgical repair is the most common management, with ligation as a second option in emergencies such as rupture. The related literature is discussed. We conclude that crural aneurysms should be considered in differential diagnosis of popliteal DVT in adults. True crural aneurysms need vigilance and a more systematical approach to provide physicians the means to the best medical care.

  7. 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.

  8. Mitochondrial-derived reactive oxygen species (ROS) play a causal role in aging-related intervertebral disc degeneration.

    PubMed

    Nasto, Luigi A; Robinson, Andria R; Ngo, Kevin; Clauson, Cheryl L; Dong, Qing; St Croix, Claudette; Sowa, Gwendolyn; Pola, Enrico; Robbins, Paul D; Kang, James; Niedernhofer, Laura J; Wipf, Peter; Vo, Nam V

    2013-07-01

    Oxidative damage is a well-established driver of aging. Evidence of oxidative stress exists in aged and degenerated discs, but it is unclear how it affects disc metabolism. In this study, we first determined whether oxidative stress negatively impacts disc matrix metabolism using disc organotypic and cell cultures. Mouse disc organotypic culture grown at atmospheric oxygen (20% O(2)) exhibited perturbed disc matrix homeostasis, including reduced proteoglycan synthesis and enhanced expression of matrix metalloproteinases, compared to discs grown at low oxygen levels (5% O(2)). Human disc cells grown at 20% O(2) showed increased levels of mitochondrial-derived superoxide anions and perturbed matrix homeostasis. Treatment of disc cells with the mitochondria-targeted reactive oxygen species (ROS) scavenger XJB-5-131 blunted the adverse effects caused by 20% O(2). Importantly, we demonstrated that treatment of accelerated aging Ercc1(-/Δ) mice, previously established to be a useful in vivo model to study age-related intervertebral disc degeneration (IDD), also resulted in improved disc total glycosaminoglycan content and proteoglycan synthesis. This demonstrates that mitochondrial-derived ROS contributes to age-associated IDD in Ercc1(-/Δ) mice. Collectively, these data provide strong experimental evidence that mitochondrial-derived ROS play a causal role in driving changes linked to aging-related IDD and a potentially important role for radical scavengers in preventing IDD.

  9. Story grammar elements and causal relations in the narratives of Russian-Hebrew bilingual children with SLI and typical language development.

    PubMed

    Fichman, Sveta; Altman, Carmit; Voloskovich, Anna; Armon-Lotem, Sharon; Walters, Joel

    2017-08-24

    While there is general agreement regarding poor performance of children with Specific Language Impairment (SLI) on microstructure measures of narrative production, findings on macrostructure are inconsistent. The present study analyzed narrative abilities of Russian-Hebrew bilingual preschool children with and without SLI, with a particular focus on story grammar (SG) elements and causal relations, in order to identify macrostructure features which distinguish bilingual children with SLI from those with typical development. Narratives were collected from 35 typically developing bilinguals (BiTD) and 14 bilinguals with SLI (BiSLI) in both Russian/L1 and Hebrew/L2 using a retelling procedure (LITMUS-Multilingual Assessment Instrument for Narratives) (Gagarina, Klop, Kunnari, Tantele, Välimaa, Balčiūnienė, Bohnacker, & Walters, 2012). Each story contained three episodes, and each episode introduced a different protagonist with explicitly stated Goals (G), Attempts (A) and Outcomes (O). Causal relations assessed included Enabling, Physical, Motivational, and Psychological relations, following Trabasso & Nickels (1992). Each Goal-Attempt-Outcome (GAO) episode was examined for the use of SG elements and causal relations. Group differences emerged for both aspects of macrostructure. For causal relations, narratives of BiSLI children contained fewer Enabling and Physical relations, and differed qualitatively from those of BiTD children. For SG elements, BiSLI children referred to fewer SG elements than BiTD children in the first episode, but performed like BiTD children in the second and the third episodes. Story grammar elements in specific episodes along with Enabling and Physical causal relations distinguish the narratives of children with BiSLI from those with BiTD, which stresses the importance of examining wider array of macrostructure features in narratives. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. 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

  11. Human factors identification and classification related to accidents'causality on hand injuries in the manufacturing industry.

    PubMed

    Reyes-Martínez, Rosa María; Maldonado-Macías, Aide; Prado-León, Lilia Roselia

    2012-01-01

    The causes of occupational accidents from the perspective of human factors have been a subject which has received little attention into the field of scientific research. The aim of this research was to identify and classify the human factors that influence human errors and failures that cause accidents and injuries specifically on hands. Available studies related to the topic have been developed mainly for aerospace applications and are found insufficient to explain accidents causalities in the manufacturing industry. This research was developed in the assembly industry of automotive harnesses and was conducted following a mixed Cognitive Anthropological approach. This study was developed in two phases. During the first qualitative phase, participants freely listed their knowledge to identify elements of the cultural domain, then and in the second phase they performed the successive pile sort technique for the collection data to classify elements in the cultural domain. Statistical models like Cluster Analysis and Multidimensional Scaling were applied for results' validation purposes. As results, 70 different human factors were identified and in the second phase they were classified into 4 main categories which were: human error, unsafe conditions, individual factors, and organizational factors. Statistical methods validated these results.

  12. 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.…

  13. Causal inference in public health.

    PubMed

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

    2013-01-01

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

  14. Topological Causality in Dynamical Systems

    NASA Astrophysics Data System (ADS)

    Harnack, Daniel; Laminski, Erik; Schünemann, Maik; Pawelzik, Klaus Richard

    2017-09-01

    Determination of causal relations among observables is of fundamental interest in many fields dealing with complex systems. Since nonlinear systems generically behave as wholes, classical notions of causality assuming separability of subsystems often turn out inadequate. Still lacking is a mathematically transparent measure of the magnitude of effective causal influences in cyclic systems. For deterministic systems we found that the expansions of mappings among time-delay state space reconstructions from different observables not only reflect the directed coupling strengths, but also the dependency of effective influences on the system's temporally varying state. Estimation of the expansions from pairs of time series is straightforward and used to define novel causality indices. Mathematical and numerical analysis demonstrate that they reveal the asymmetry of causal influences including their time dependence, as well as provide measures for the effective strengths of causal links in complex systems.

  15. Are stressful life events causally related to the severity of obsessive-compulsive symptoms? A monozygotic twin difference study

    PubMed Central

    Vidal-Ribas, P.; Stringaris, A.; Rück, C.; Serlachius, E.; Lichtenstein, P.; Mataix-Cols, D.

    2015-01-01

    Traumatic or stressful life events have long been hypothesized to play a role in causing or precipitating obsessive-compulsive symptoms but the impact of these environmental factors has rarely been investigated using genetically informative designs. We tested whether a wide range of retrospectively-reported stressful life events (SLEs) influence the lifetime presence and severity of obsessive-compulsive symptoms (OCS) in a large Swedish population-based cohort of 22,084 twins. Multiple regression models examined whether differences in SLEs within twin pairs were significantly associated with differences in OCS. In the entire sample (i.e., both monozygotic [MZ] and dizygotic twin pairs), two SLEs factors, “abuse and family disruption” and “sexual abuse”, were significantly associated with the severity of OCS even after controlling for depressive symptoms. Other SLEs factors were either not associated with OCS (“loss”, “non-sexual assault”) or were no longer associated with OCS after controlling for depression (“illness/injury”). Within MZ pair analyses, which effectively control for genetic and shared environmental effects, showed that only the “abuse and family disruption” factor remained independently related to within-pair differences in OCS severity, even after controlling for depressive symptoms. Despite being statistically significant, the magnitude of the associations was small; “abuse and family disruption” explained approximately 3% of the variance in OCS severity. We conclude that OCS are selectively associated with certain types of stressful life events. In particular, a history of interpersonal abuse, neglect and family disruption may make a modest but significant contribution to the severity of OCS. Further replication in longitudinal cohorts is essential before causality can be firmly established. PMID:25511316

  16. Are stressful life events causally related to the severity of obsessive-compulsive symptoms? A monozygotic twin difference study.

    PubMed

    Vidal-Ribas, P; Stringaris, A; Rück, C; Serlachius, E; Lichtenstein, P; Mataix-Cols, D

    2015-02-01

    Traumatic or stressful life events have long been hypothesized to play a role in causing or precipitating obsessive-compulsive symptoms but the impact of these environmental factors has rarely been investigated using genetically informative designs. We tested whether a wide range of retrospectively-reported stressful life events (SLEs) influence the lifetime presence and severity of obsessive-compulsive symptoms (OCS) in a large Swedish population-based cohort of 22,084 twins. Multiple regression models examined whether differences in SLEs within twin pairs were significantly associated with differences in OCS. In the entire sample (i.e., both monozygotic [MZ] and dizygotic twin pairs), two SLEs factors, "abuse and family disruption" and "sexual abuse", were significantly associated with the severity of OCS even after controlling for depressive symptoms. Other SLEs factors were either not associated with OCS ("loss", "non-sexual assault") or were no longer associated with OCS after controlling for depression ("illness/injury"). Within MZ pair analyses, which effectively control for genetic and shared environmental effects, showed that only the "abuse and family disruption" factor remained independently related to within-pair differences in OCS severity, even after controlling for depressive symptoms. Despite being statistically significant, the magnitude of the associations was small; "abuse and family disruption" explained approximately 3% of the variance in OCS severity. We conclude that OCS are selectively associated with certain types of stressful life events. In particular, a history of interpersonal abuse, neglect and family disruption may make a modest but significant contribution to the severity of OCS. Further replication in longitudinal cohorts is essential before causality can be firmly established. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

  17. A Storybook Method for Exploring Young Children's Views of Illness Causality in Relation to the Familial Context

    ERIC Educational Resources Information Center

    McIntosh, Caroline; Stephens, Christine

    2012-01-01

    In this paper we describe a method for exploring young children's views of illness causality in social context. Studies of children's conceptualisation of illness have predominantly focused on the nature of children's knowledge rather than locating that knowledge within socio-cultural contexts. Adopting a socio-constructivist perspective we sought…

  18. 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…

  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. Sensing the coherence of biology in contrast to psychology: young children's use of causal relations to distinguish two foundational domains.

    PubMed

    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 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.

  1. 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

  2. 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.

  3. 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

  4. Effect of measurement noise on Granger causality

    NASA Astrophysics Data System (ADS)

    Nalatore, Hariharan; N, Sasikumar; 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.

  5. 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.

  6. 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.

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

    PubMed

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

    2017-07-01

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

  8. Exposure to traffic-related air pollution during pregnancy and term low birth weight: estimation of causal associations in a semiparametric model.

    PubMed

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

    2012-11-01

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

  9. 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

  10. Causal evolution of wave packets

    NASA Astrophysics Data System (ADS)

    Eckstein, Michał; Miller, Tomasz

    2017-03-01

    Drawing from the optimal transport theory adapted to the relativistic setting we formulate the principle of a causal flow of probability and apply it in the wave-packet formalism. We demonstrate that whereas the Dirac Hamiltonian impels a causal evolution of probabilities, even in the presence of interactions, the relativistic-Schrödinger model is acausal. We quantify the causality breakdown in the latter model and argue that, in contrast to the popular viewpoint, it is not related to the localization properties of the states.

  11. 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…

  12. Quantifying causal coupling strength: A lag-specific measure for multivariate time series related to transfer entropy

    NASA Astrophysics Data System (ADS)

    Runge, Jakob; Heitzig, Jobst; Marwan, Norbert; Kurths, Jürgen

    2012-12-01

    While it is an important problem to identify the existence of causal associations between two components of a multivariate time series, a topic addressed in Runge, Heitzig, Petoukhov, and Kurths [Phys. Rev. Lett.PRLTAO0031-900710.1103/PhysRevLett.108.258701 108, 258701 (2012)], it is even more important to assess the strength of their association in a meaningful way. In the present article we focus on the problem of defining a meaningful coupling strength using information-theoretic measures and demonstrate the shortcomings of the well-known mutual information and transfer entropy. Instead, we propose a certain time-delayed conditional mutual information, the momentary information transfer (MIT), as a lag-specific measure of association that is general, causal, reflects a well interpretable notion of coupling strength, and is practically computable. Rooted in information theory, MIT is general in that it does not assume a certain model class underlying the process that generates the time series. As discussed in a previous paper [Runge, Heitzig, Petoukhov, and Kurths, Phys. Rev. Lett.PRLTAO0031-900710.1103/PhysRevLett.108.258701 108, 258701 (2012)], the general framework of graphical models makes MIT causal in that it gives a nonzero value only to lagged components that are not independent conditional on the remaining process. Further, graphical models admit a low-dimensional formulation of conditions, which is important for a reliable estimation of conditional mutual information and, thus, makes MIT practically computable. MIT is based on the fundamental concept of source entropy, which we utilize to yield a notion of coupling strength that is, compared to mutual information and transfer entropy, well interpretable in that, for many cases, it solely depends on the interaction of the two components at a certain lag. In particular, MIT is, thus, in many cases able to exclude the misleading influence of autodependency within a process in an information-theoretic way

  13. 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

  14. Inactive matrix Gla protein is causally related to adverse health outcomes: a Mendelian randomization study in a Flemish population.

    PubMed

    Liu, Yan-Ping; Gu, Yu-Mei; Thijs, Lutgarde; Knapen, Marjo H J; Salvi, Erika; Citterio, Lorena; Petit, Thibault; Carpini, Simona Delli; Zhang, Zhenyu; Jacobs, Lotte; Jin, Yu; Barlassina, Cristina; Manunta, Paolo; Kuznetsova, Tatiana; Verhamme, Peter; Struijker-Boudier, Harry A; Cusi, Daniele; Vermeer, Cees; Staessen, Jan A

    2015-02-01

    Matrix Gla-protein is a vitamin K-dependent protein that strongly inhibits arterial calcification. Vitamin K deficiency leads to production of inactive nonphosphorylated and uncarboxylated matrix Gla protein (dp-ucMGP). The risk associated with dp-ucMGP in the population is unknown. In a Flemish population study, we measured circulating dp-ucMGP at baseline (1996-2011), genotyped MGP, recorded adverse health outcomes until December 31, 2012, and assessed the multivariable-adjusted associations of adverse health outcomes with dp-ucMGP. We applied a Mendelian randomization analysis using MGP genotypes as instrumental variables. Among 2318 participants, baseline dp-ucMGP averaged 3.61 μg/L. Over 14.1 years (median), 197 deaths occurred, 58 from cancer and 70 from cardiovascular disease; 85 participants experienced a coronary event. The risk of death and non-cancer mortality curvilinearly increased (P≤0.008) by 15.0% (95% confidence interval, 6.9-25.3) and by 21.5% (11.1-32.9) for a doubling of the nadir (1.43 and 0.97 μg/L, respectively). With higher dp-ucMGP, cardiovascular mortality log-linearly increased (hazard ratio for dp-ucMGP doubling, 1.14 [1.01-1.28]; P=0.027), but coronary events log-linearly decreased (0.93 [0.88-0.99]; P=0.021). dp-ucMGP levels were associated (P≤0.001) with MGP variants rs2098435, rs4236, and rs2430692. For non-cancer mortality and coronary events (P≤0.022), but not for total and cardiovascular mortality (P≥0.13), the Mendelian randomization analysis suggested causality. Higher dp-ucMGP predicts total, non-cancer and cardiovascular mortality, but lower coronary risk. For non-cancer mortality and coronary events, these associations are likely causal.

  15. Dynamic causal modelling revisited.

    PubMed

    Friston, K J; Preller, Katrin H; Mathys, Chris; Cagnan, Hayriye; Heinzle, Jakob; Razi, Adeel; Zeidman, Peter

    2017-02-17

    This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. This provides a generative or dynamic causal model of laminar specific responses that can generate haemodynamic and electrophysiological measurements. In principle, this allows the fusion of haemodynamic and (event related or induced) electrophysiological responses. Furthermore, it enables Bayesian model comparison of competing hypotheses about physiologically plausible synaptic effects; for example, does attentional modulation act on superficial or deep pyramidal cells - or both? In this technical note, we describe the resulting dynamic causal model and provide an illustrative application to the attention to visual motion dataset used in previous papers. Our focus here is on how to answer long-standing questions in fMRI; for example, do haemodynamic responses reflect extrinsic (afferent) input from distant cortical regions, or do they reflect intrinsic (recurrent) neuronal activity? To what extent do inhibitory interneurons contribute to neurovascular coupling? What is the relationship between haemodynamic responses and the frequency of induced neuronal activity? This paper does not pretend to answer these questions; rather it shows how they can be addressed using neural mass models of fMRI timeseries.

  16. A Theory of Causal Learning in Children: Causal Maps and Bayes Nets

    ERIC Educational Resources Information Center

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

    2004-01-01

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

  17. The expression of P-glycoprotein is causally related to a less aggressive phenotype in human osteosarcoma cells.

    PubMed

    Scotlandi, K; Manara, M C; Serra, M; Benini, S; Maurici, D; Caputo, A; De Giovanni, C; Lollini, P L; Nanni, P; Picci, P; Campanacci, M; Baldini, N

    1999-01-21

    The relationship between P-glycoprotein expression and malignancy is controversial. We have recently found that, in osteosarcoma, multidrug resistance (MDR) is associated with a less aggressive behavior, both in vitro and in clinical settings. In this study, we evaluated whether P-glycoprotein overexpression has a cause-effect relationship with the reduced metastatic potential of MDR cells, or rather reflects a more complex phenotype. MDR1 gene-transfected osteosarcoma cell clones, showing different levels of P-glycoprotein expression, were analysed for their in vitro characteristics and their tumorigenic and metastatic ability in athymic mice. Apart from the different levels of P-glycoprotein, no significant change in the expression of surface antigens or in the differentiative features were observed in the MDR1 gene transfectants compared to the parental cell lines or control clones, obtained by transfection with neo gene alone. In contrast to controls, however, MDR1 transfectants showed a significantly lower ability to grow in semi-solid medium and were completely unable to grow and give lung metastases in athymic mice. These findings indicate that P-glycoprotein overexpression is causally associated with a low malignant potential of osteosarcoma cells, and open new insights on the role and functions of P-glycoprotein activity.

  18. On the Causality and K-Causality between Measures

    NASA Astrophysics Data System (ADS)

    Miller, Tomasz

    2017-03-01

    Drawing from our earlier works on the notion of causality for nonlocal phenomena, we propose and study the extension of the Sorkin--Woolgar relation $K^+$ onto the space of Borel probability measures on a given spacetime. We show that it retains its fundamental properties of transitivity and closedness. Furthermore, we list and prove several characterizations of this relation, including the `nonlocal' analogue of the characterization of $K^+$ in terms of time functions. This generalizes and casts new light on our earlier results concerning the causal precedence relation $J^+$ between measures.

  19. Experimental verification of an indefinite causal order

    PubMed Central

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

    2017-01-01

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

  20. Independence and dependence in human causal reasoning.

    PubMed

    Rehder, Bob

    2014-07-01

    Causal graphical models (CGMs) are a popular formalism used to model human causal reasoning and learning. The key property of CGMs is the causal Markov condition, which stipulates patterns of independence and dependence among causally related variables. Five experiments found that while adult's causal inferences exhibited aspects of veridical causal reasoning, they also exhibited a small but tenacious tendency to violate the Markov condition. They also failed to exhibit robust discounting in which the presence of one cause as an explanation of an effect makes the presence of another less likely. Instead, subjects often reasoned "associatively," that is, assumed that the presence of one variable implied the presence of other, causally related variables, even those that were (according to the Markov condition) conditionally independent. This tendency was unaffected by manipulations (e.g., response deadlines) known to influence fast and intuitive reasoning processes, suggesting that an associative response to a causal reasoning question is sometimes the product of careful and deliberate thinking. That about 60% of the erroneous associative inferences were made by about a quarter of the subjects suggests the presence of substantial individual differences in this tendency. There was also evidence that inferences were influenced by subjects' assumptions about factors that disable causal relations and their use of a conjunctive reasoning strategy. Theories that strive to provide high fidelity accounts of human causal reasoning will need to relax the independence constraints imposed by CGMs.

  1. Adoption of risk-related factors through early adolescence: associations with weight status and implications for causal mechanisms.

    PubMed

    Pasch, Keryn E; Nelson, Melissa C; Lytle, Leslie A; Moe, Stacey G; Perry, Cheryl L

    2008-10-01

    To examine cross-sectional and longitudinal associations between weight status and measures of risk and protective factors in youth. Participants included 3010 students (72.1% white, 27.9% nonwhite), with a baseline mean age of 12.7 years from the Teens Eating for Energy and Nutrition at School (TEENS) study. Surveys were administered in grades 7 and 8. Cross-sectional and longitudinal mixed-effects regression analyses were conducted to determine the association between body mass index z-score percentiles (BMI) and risk and protective factors (including substance use, depression, fighting, optimism, and spirituality). Only depression was associated with BMI at the beginning of grade 7. However, by the end of grade 8, binge drinking, alcohol, tobacco, and other drug (ATOD) use, fighting, and depression were all cross-sectionally associated with BMI. Longitudinally, BMI in grade 7 did not predict risk and protective factors in grade 8. However, ATOD use, fighting, depression, and optimism in grade 7 predicted BMI in grade 8. This study suggests there is a notable co-occurrence of unhealthy factors (including weight status, ATOD use, depression) which appears to develop during the critical transition period through early adolescence. Specifically, earlier ATOD use, depression, increased fighting, and decreased optimism may lead to unhealthy increases in weight status, whereas early indicators of increased weight status do not appear to predict increases in these factors. This work yields important insights into the causal mechanisms underlying adolescent behavior patterning and the progression with which these unhealthy risk factor profiles are adopted during this critical age.

  2. Using RNA-Seq SNP data to reveal potential causal mutations related to pig production traits and RNA editing.

    PubMed

    Martínez-Montes, A M; Fernández, A; Pérez-Montarelo, D; Alves, E; Benítez, R M; Nuñez, Y; Óvilo, C; Ibañez-Escriche, N; Folch, J M; Fernández, A I

    2017-04-01

    RNA-Seq technology is widely used in quantitative gene expression studies and identification of non-annotated transcripts. However this technology also can be used for polymorphism detection and RNA editing in transcribed regions in an efficient and cost-effective way. This study used SNP data from an RNA-Seq assay to identify genes and mutations underlying production trait variations in an experimental pig population. The hypothalamic and hepatic transcriptomes of nine extreme animals for growth and fatness from an (Iberian × Landrace) × Landrace backcross were analyzed by RNA-Seq methodology, and SNP calling was conducted. More than 125 000 single nucleotide variants (SNVs) were identified in each tissue, and 78% were considered to be potential SNPs, those SNVs segregating in the context of this study. Potential informative SNPs were detected by considering those showing a homozygous or heterozygous genotype in one extreme group and the alternative genotype in the other group. In this way, 4396 and 1862 informative SNPs were detected in hypothalamus and liver respectively. Out of the 32 SNPs selected for validation, 25 (80%) were confirmed as actual SNPs. Association analyses for growth, fatness and premium cut yields with 19 selected SNPs were carried out, and four potential causal genes (RETSAT, COPA, RNMT and PALMD) were identified. Interestingly, new RNA editing modifications were detected and validated for the NR3C1:g.102797 (ss1985401074) and ACSM2B:g.13374 (ss1985401075) positions and for the COG3:g3.4525 (ss1985401087) modification previously identified across vertebrates, which could lead to phenotypic variation and should be further investigated.

  3. 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

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

    PubMed

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

    2014-09-01

    Risk for drug abuse (DA) is strongly associated with neighborhood social deprivation (SD). However, the causal nature of this relationship is unclear. Three Swedish population-based cohorts were followed up over 5 years for incident registration of DA in medical, legal or pharmacy records. In each cohort, we examined the SD-DA association, controlling carefully for individual socio-economic status (SES) with multiple measures, in the entire sample and among pairs of first cousins, paternal and maternal half-siblings, full siblings and monozygotic (MZ) twins discordant for SD exposure. The number of informative relative pairs ranged from 6366 to 166,208. In all cohorts, SD was prospectively related to risk for incident DA. In relative pairs discordant for SD exposure, the SD-DA association was similar to that seen in the entire population in cousins, half-siblings, full siblings and MZ twins. Eliminating subjects who were residentially unstable or had DA in the first two follow-up years did not alter this pattern. When divided by age, in the youngest groups, the SD-DA association was weaker in siblings than in the entire population. Across three cohorts, controlling for individual SES and confounding familial factors, SD prospectively predicted risk for incident DA registration. These results support the hypothesis that the SD-DA association is in part causal and unlikely to result entirely from personal attributes, which both increase risk for DA and cause selection into high SD environments. At least part of the SD-DA association arises because exposure to SD causes an increased risk of DA.

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

    PubMed Central

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

    2014-01-01

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

  6. 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.

  7. 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.

  8. Causal Imprinting in Causal Structure Learning

    PubMed Central

    Taylor, Eric G.; Ahn, Woo-kyoung

    2012-01-01

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

  9. Causal imprinting in causal structure learning.

    PubMed

    Taylor, Eric G; Ahn, Woo-Kyoung

    2012-11-01

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

  10. Analyzing multiple nonlinear time series with extended Granger causality

    NASA Astrophysics Data System (ADS)

    Chen, Yonghong; Rangarajan, Govindan; Feng, Jianfeng; Ding, Mingzhou

    2004-04-01

    Identifying causal relations among simultaneously acquired signals is an important problem in multivariate time series analysis. For linear stochastic systems Granger proposed a simple procedure called the Granger causality to detect such relations. In this work we consider nonlinear extensions of Granger's idea and refer to the result as extended Granger causality. A simple approach implementing the extended Granger causality is presented and applied to multiple chaotic time series and other types of nonlinear signals. In addition, for situations with three or more time series we propose a conditional extended Granger causality measure that enables us to determine whether the causal relation between two signals is direct or mediated by another process.

  11. The role of individual and personality factors in controlling risky behaviours related to AIDS: Proposing a causal model.

    PubMed

    Rezaei, Mansour; Zakiei, Ali; Reshadat, Soheyla; Ghasemi, Seyed Ramin

    2017-02-01

    Investigating previous studies show that personality traits have an important role in controlling risky behaviours related to AIDS; therefore, the aim of this study is to investigate the relationship between AIDS health literacy, personality traits and mental health and controlling risky behaviours related to AIDS through self-efficacy. The statistical population includes all the young people in western provinces of Iran, 2015. Data analysis was carried out for a sample of 756 participants (59% female). The results show that except for the socializing trait, all the other variables are related to controlling risky behaviours. In addition, variables of health literacy related to AIDS, mental health, activity, impulsive sensation seeking and hostility have a direct relation to controlling risky behaviours. Also, the predicting behaviours can predict 62% of the variance in controlling risky behaviours related to AIDS. The analysis results show that health literacy has an indirect impact on controlling risky behaviours through self-efficacy. In other words, health literacy related to AIDS leads to controlling risky behaviours when self-efficacy is high for controlling risky behaviours. Based on the results, it is recommended that the role of self-efficacy in controlling risky behaviours be considered as a strategy for preventing AIDS. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  12. Compound treatments and transportability of causal inference.

    PubMed

    Hernán, Miguel A; VanderWeele, Tyler J

    2011-05-01

    Ill-defined causal questions present serious problems for observational studies-problems that are largely unappreciated. This paper extends the usual counterfactual framework to consider causal questions about compound treatments for which there are many possible implementations (for example, "prevention of obesity"). We describe the causal effect of compound treatments and their identifiability conditions, with a special emphasis on the consistency condition. We then discuss the challenges of using the estimated effect of a compound treatment in one study population to inform decisions in the same population and in other populations. These challenges arise because the causal effect of compound treatments depends on the distribution of the versions of treatment in the population. Such causal effects can be unpredictable when the versions of treatment are unknown. We discuss how such issues of "transportability" are related to the consistency condition in causal inference. With more carefully framed questions, the results of epidemiologic studies can be of greater value to decision-makers.

  13. Causal analysis of academic performance.

    PubMed

    Rao, D C; Morton, N E; Elston, R C; Yee, S

    1977-03-01

    Maximum likelihood methods are presented to test for the relations between causes and effects in linear path diagrams, without assuming that estimates of causes are free of error. Causal analysis is illustrated by published data of the Equal Educational Opportunity Survey, which show that American schools do not significantly modify socioeconomic differences in academic performance and that little of the observed racial difference in academic performance is causal. For two races differing by 15 IQ points, the differential if social class were randomized would be only about 3 points. The principle is stressed that a racial effect in a causal system may be environmental and that its etiology can be studied only by analysis of family resemblance in hybrid populations.

  14. 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…

  15. Causal density matrices

    NASA Astrophysics Data System (ADS)

    Engelhardt, Netta; Fischetti, Sebastian

    2017-06-01

    We define a new construct in quantum field theory—the causal density matrix—obtained from the singularity structure of correlators of local operators. This object provides a necessary and sufficient condition for a quantum field theory state to have a holographic semiclassical dual causal geometry. By exploiting the causal density matrix, we find that these dual causal geometries quite generally (even away from AdS /CFT ) exhibit features of quantum error correction. Within AdS /CFT , we argue that the "reduced" causal density matrix is the natural dual to the causal wedge. Our formalism is very well-suited to generalizations of holography beyond AdS /CFT or even gravity/QFT.

  16. 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.; 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.

  17. Information Theoretic Causal Coordination

    DTIC Science & Technology

    2013-09-12

    his 1969 paper, Clive Granger , British economist and Nobel laureate, proposed a statistical def- inition of causality between stochastic processes. It...showed that the directed infor- mation, an information theoretic quantity, quantifies Granger causality . We also explored a more pessimistic setup...Final Technical Report Project Title: Information Theoretic Causal Coordination AFOSR Award Number: AF FA9550-10-1-0345 Reporting Period: July 15

  18. Multisource causal data mining

    NASA Astrophysics Data System (ADS)

    Woodley, Robert; Gosnell, Michael; Shallenberger, Kevin

    2012-06-01

    Analysts are faced with mountains of data, and finding that relevant piece of information is the proverbial needle in a haystack, only with dozens of haystacks. Analysis tools that facilitate identifying causal relationships across multiple data sets are sorely needed. 21st Century Systems, Inc. (21CSi) has initiated research called Causal-View, a causal datamining visualization tool, to address this challenge. Causal-View is built on an agent-enabled framework. Much of the processing that Causal-View will do is in the background. When a user requests information, Data Extraction Agents launch to gather information. This initial search is a raw, Monte Carlo type search designed to gather everything available that may have relevance to an individual, location, associations, and more. This data is then processed by Data- Mining Agents. The Data-Mining Agents are driven by user supplied feature parameters. If the analyst is looking to see if the individual frequents a known haven for insurgents he may request information on his last known locations. Or, if the analyst is trying to see if there is a pattern in the individual's contacts, the mining agent can be instructed with the type and relevance of the information fields to look at. The same data is extracted from the database, but the Data Mining Agents customize the feature set to determine causal relationships the user is interested in. At this point, a Hypothesis Generation and Data Reasoning Agents take over to form conditional hypotheses about the data and pare the data, respectively. The newly formed information is then published to the agent communication backbone of Causal- View to be displayed. Causal-View provides causal analysis tools to fill the gaps in the causal chain. We present here the Causal-View concept, the initial research into data mining tools that assist in forming the causal relationships, and our initial findings.

  19. Classical sequential growth dynamics for causal sets

    NASA Astrophysics Data System (ADS)

    Rideout, D. P.; Sorkin, R. D.

    2000-01-01

    Starting from certain causality conditions and a discrete form of general covariance, we derive a very general family of classically stochastic, sequential growth dynamics for causal sets. The resulting theories provide a relatively accessible ``halfway house'' to full quantum gravity that possibly contains the latter's classical limit (general relativity). Because they can be expressed in terms of state models for an assembly of Ising spins residing on the relations of the causal set, these theories also illustrate how nongravitational matter can arise dynamically from the causal set without having to be built in at the fundamental level. Additionally, our results bring into focus some interpretive issues of importance for a causal set dynamics and for quantum gravity more generally.

  20. 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.

  1. Causality and Composite Structure

    SciTech Connect

    Joglekar, Satish D.

    2007-10-03

    In this talk, we discuss the question of whether a composite structure of elementary particles, with a length scale 1/{lambda}, can leave observable effects of non-locality and causality violation at higher energies (but {<=}{lambda}); employing a model-independent approach based on Bogoliubov-Shirkov formulation of causality. We formulate a condition which must be fulfilled for the derived theory to be causal, if the fundamental theory is so; and analyze it to exhibit possibilities which fulfil and which violate the condition. We comment on how causality violating amplitudes can arise.

  2. 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

  3. 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.

  4. Establishing Causal Coherence across Sentences: An ERP Study

    ERIC Educational Resources Information Center

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

    2011-01-01

    This study examined neural activity associated with establishing causal relationships across sentences during on-line 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…

  5. Establishing Causal Coherence across Sentences: An ERP Study

    ERIC Educational Resources Information Center

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

    2011-01-01

    This study examined neural activity associated with establishing causal relationships across sentences during on-line 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…

  6. Causal knowledge and the development of inductive reasoning.

    PubMed

    Bright, Aimée K; Feeney, Aidan

    2014-06-01

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

  7. Causal relationship between obesity-related traits and TLR4-driven responses at the maternal-fetal interface.

    PubMed

    Yang, Xiaohua; Li, Ming; Haghiac, Maricela; Catalano, Patrick M; O'Tierney-Ginn, Perrie; Hauguel-de Mouzon, Sylvie

    2016-11-01

    Obesity triggers complex inflammatory networks within the innate immune system. During pregnancy, the placenta amplifies the low-grade inflammation through activation of Toll-like receptor 4 (TLR4) signalling pathways. The purpose of this study was to investigate the impact of obesity on placental TLR4 expression and inflammatory signals. The secondary aim was to analyse the placental cell type responsible for TLR4 activation. Thirty-nine women recruited at term-scheduled Caesarean section were grouped according to their pre-gravid BMI (<25 kg/m(2) and >30 kg/m(2)). Placenta, venous maternal and cord blood were obtained at delivery for analysis. Data were analysed with linear regression and Spearman's rank correlation coefficient analysis. TLR4, IL6 and IL8 expression was increased three- to ninefold (p < 0.001) in the placenta of obese vs lean women. There was a positive correlation between placental TLR4 and maternal systemic and placental IL6 and IL8 concentrations. Placental TLR4 expression was correlated with maternal pre-gravid BMI, insulin resistance index, plasma insulin and C-reactive protein (r = 0.57, 0.31, 0.35, 0.53, respectively; p < 0.001) but not with plasma glucose, maternal age, gestational age and gestational weight gain (r < 0.2; p > 0.1). TLR4 was located in both trophoblast and macrovascular endothelial cells lining fetal vasculature. Lipopolysaccharide-induced TLR4 activation was more robust in trophoblasts than in endothelial vascular cells (100-fold vs tenfold; p < 0.001). Trophoblastic TLR4 is strongly implicated in the propagation of placental inflammation. Placental inflammation is related to maternal metabolic conditions such as pre-gravid BMI, whilst gestational weight gain or gestational age are not. These results implicate the pre-gravid condition as a significant contributor to metabolic inflammation in late pregnancy.

  8. The handling of causality in SEA guidance

    SciTech Connect

    Perdicoulis, A. . E-mail: tasso@utad.pt; Hanusch, M. . E-mail: marie.hanusch@ufz.de; Kasperidus, H.D. . E-mail: hans.kasperidus@ufz.de; Weiland, U. . E-mail: ulrike.weiland@ufz.de

    2007-03-15

    Causality, or the relation of cause and effect, is a fundamental notion in science, planning, and management. It is also a fundamental notion in impact assessment, as it relates action proposals with environmental impacts. Building on recent research about causality in impact assessment, this article turns to Strategic Environmental Assessment (SEA) and examines eleven guidance documents to see whether and how they handle causality. The findings of this research, in line with results from similar research in EIA, indicate a weak handling of causality with regards to instruction, obligation, and theoretical support. The recommendations are to investigate the reasons for the observed trend, as there is no evidence in the guidance documents themselves, and to have the fundamental premise of causality in impact assessment safeguarded in future SEA guidance.

  9. Preschool children learn about causal structure from conditional interventions.

    PubMed

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

    2007-05-01

    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 and interactive causal structures even in the absence of differential spatiotemporal cues and specific mechanism knowledge. Children were also able to use knowledge of causal structure to predict the patterns of evidence that would result from interventions. A third study suggests that children's spontaneous play can generate evidence that would support such accurate causal learning.

  10. 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…

  11. 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…

  12. 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…

  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. 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

  15. Agency, time, and causality.

    PubMed

    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.

  16. Quantum causal graph dynamics

    NASA Astrophysics Data System (ADS)

    Arrighi, Pablo; Martiel, Simon

    2017-07-01

    Consider a graph having quantum systems lying at each node. Suppose that the whole thing evolves in discrete time steps, according to a global, unitary causal operator. By causal we mean that information can only propagate at a bounded speed, with respect to the distance given by the graph. Suppose, moreover, that the graph itself is subject to the evolution, and may be driven to be in a quantum superposition of graphs—in accordance to the superposition principle. We show that these unitary causal operators must decompose as a finite-depth circuit of local unitary gates. This unifies a result on quantum cellular automata with another on reversible causal graph dynamics. Along the way we formalize a notion of causality which is valid in the context of quantum superpositions of time-varying graphs, and has a number of good properties. We discuss some of the implications for quantum gravity.

  17. 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.

  18. A nonlinear generalization of spectral Granger causality.

    PubMed

    He, Fei; Wei, Hua-Liang; Billings, Stephen A; Sarrigiannis, Ptolemaios G

    2014-06-01

    Spectral measures of linear Granger causality have been widely applied to study the causal connectivity between time series data in neuroscience, biology, and economics. Traditional Granger causality measures are based on linear autoregressive with exogenous (ARX) inputs models of time series data, which cannot truly reveal nonlinear effects in the data especially in the frequency domain. In this study, it is shown that the classical Geweke's spectral causality measure can be explicitly linked with the output spectra of corresponding restricted and unrestricted time-domain models. The latter representation is then generalized to nonlinear bivariate signals and for the first time nonlinear causality analysis in the frequency domain. This is achieved by using the nonlinear ARX (NARX) modeling of signals, and decomposition of the recently defined output frequency response function which is related to the NARX model.

  19. 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…

  20. 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…

  1. Multivariate Granger causality and generalized variance.

    PubMed

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

    2010-04-01

    Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables but may occur among groups or "ensembles" of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke's seminal 1982 work, we offer additional justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define "partial" Granger causality in the multivariate context and we also motivate reformulations of "causal density" and "Granger autonomy." Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise.

  2. Multivariate Granger causality and generalized variance

    NASA Astrophysics Data System (ADS)

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

    2010-04-01

    Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables but may occur among groups or “ensembles” of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke’s seminal 1982 work, we offer additional justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define “partial” Granger causality in the multivariate context and we also motivate reformulations of “causal density” and “Granger autonomy.” Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise.

  3. Causality and headache triggers

    PubMed Central

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

    2013-01-01

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

  4. Anterior cingulate cortex-related connectivity in first-episode schizophrenia: a spectral dynamic causal modeling study with functional magnetic resonance imaging

    PubMed Central

    Cui, Long-Biao; Liu, Jian; Wang, Liu-Xian; Li, Chen; Xi, Yi-Bin; Guo, Fan; Wang, Hua-Ning; Zhang, Lin-Chuan; Liu, Wen-Ming; He, Hong; Tian, Ping; Yin, Hong; Lu, Hongbing

    2015-01-01

    Understanding the neural basis of schizophrenia (SZ) is important for shedding light on the neurobiological mechanisms underlying this mental disorder. Structural and functional alterations in the anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), hippocampus, and medial prefrontal cortex (MPFC) have been implicated in the neurobiology of SZ. However, the effective connectivity among them in SZ remains unclear. The current study investigated how neuronal pathways involving these regions were affected in first-episode SZ using functional magnetic resonance imaging (fMRI). Forty-nine patients with a first-episode of psychosis and diagnosis of SZ—according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision—were studied. Fifty healthy controls (HCs) were included for comparison. All subjects underwent resting state fMRI. We used spectral dynamic causal modeling (DCM) to estimate directed connections among the bilateral ACC, DLPFC, hippocampus, and MPFC. We characterized the differences using Bayesian parameter averaging (BPA) in addition to classical inference (t-test). In addition to common effective connectivity in these two groups, HCs displayed widespread significant connections predominantly involved in ACC not detected in SZ patients, but SZ showed few connections. Based on BPA results, SZ patients exhibited anterior cingulate cortico-prefrontal-hippocampal hyperconnectivity, as well as ACC-related and hippocampal-dorsolateral prefrontal-medial prefrontal hypoconnectivity. In summary, spectral DCM revealed the pattern of effective connectivity involving ACC in patients with first-episode SZ. This study provides a potential link between SZ and dysfunction of ACC, creating an ideal situation to associate mechanisms behind SZ with aberrant connectivity among these cognition and emotion-related regions. PMID:26578933

  5. 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…

  6. 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…

  7. 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…

  8. 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…

  9. [The causal relationship].

    PubMed

    Glemain, P

    2000-09-01

    Only the controlled trial method, clinical equivalent to the experimental method, with its successive phases and randomization, is able to confirm a real causal relationship and quantify the risk of error (alpha). However, the study must have sufficient power and randomization must not have resulted in an unbalanced distribution of various parameters likely to influence the result. Other methods, particularly surveys and case studies, only provide presumptions of causality. This review article, illustrated by three examples from the urological literature, is designed to demonstrate the difficulties of establishing a causal relationship when possible biases and confounding factors are taken into account.

  10. Shortcomings/Limitations of Blockwise Granger Causality and Advances of Blockwise New Causality.

    PubMed

    Hu, Sanqing; Jia, Xinxin; Zhang, Jianhai; Kong, Wanzeng; Cao, Yu

    2016-12-01

    Multivariate blockwise Granger causality (BGC) is used to reflect causal interactions among blocks of multivariate time series. In particular, spectral BGC and conditional spectral BGC are used to disclose blockwise causal flow among different brain areas in various frequencies. In this paper, we demonstrate that: 1) BGC in time domain may not necessarily disclose true causality and 2) due to the use of the transfer function or its inverse matrix and partial information of the multivariate linear regression model, both of spectral BGC and conditional spectral BGC have shortcomings and/or limitations, which may inevitably lead to misinterpretation. We then, in time and frequency domains, develop two new multivariate blockwise causality methods for the linear regression model called blockwise new causality (BNC) and spectral BNC, respectively. By several examples, we confirm that BNC measures are more reasonable and sensitive to reflect true causality or trend of true causality than BGC or conditional BGC. Finally, for electroencephalograph data from an epilepsy patient, we analyze event-related potential causality and demonstrate that both of the BGC and BNC methods show significant causality flow in frequency domain, but the spectral BNC method yields satisfactory and convincing results, which are consistent with an event-related time-frequency power spectrum activity. The spectral BGC method is shown to generate misleading results. Thus, we deeply believe that our new blockwise causality definitions as well as our previous NC definitions may have wide applications to reflect true causality among two blocks of time series or two univariate time series in economics, neuroscience, and engineering.

  11. 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

  12. Causality modeling for directed disease network.

    PubMed

    Bang, Sunjoo; Kim, Jae-Hoon; Shin, Hyunjung

    2016-09-01

    Causality between two diseases is valuable information as subsidiary information for medicine which is intended for prevention, diagnostics and treatment. Conventional cohort-centric researches are able to obtain very objective results, however, they demands costly experimental expense and long period of time. Recently, data source to clarify causality has been diversified: available information includes gene, protein, metabolic pathway and clinical information. By taking full advantage of those pieces of diverse information, we may extract causalities between diseases, alternatively to cohort-centric researches. In this article, we propose a new approach to define causality between diseases. In order to find causality, three different networks were constructed step by step. Each step has different data sources and different analytical methods, and the prior step sifts causality information to the next step. In the first step, a network defines association between diseases by utilizing disease-gene relations. And then, potential causalities of disease pairs are defined as a network by using prevalence and comorbidity information from clinical results. Finally, disease causalities are confirmed by a network defined from metabolic pathways. The proposed method is applied to data which is collected from database such as MeSH, OMIM, HuDiNe, KEGG and PubMed. The experimental results indicated that disease causality that we found is 19 times higher than that of random guessing. The resulting pairs of causal-effected diseases are validated on medical literatures. http://www.alphaminers.net shin@ajou.ac.kr Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  13. Causal networks in EIA

    SciTech Connect

    Perdicoulis, Anastassios . E-mail: tasso@utad.pt; Glasson, John . E-mail: jglasson@brookes.ac.uk

    2006-08-15

    Causal networks have been used in Environmental Impact Assessment (EIA) since its early days, but they appear to have a minimal use in modern practice. This article reviews the typology of causal networks in EIA as well as in other academic and professional fields, verifies their contribution to EIA against the principles and requirements of the process, and discusses alternative scenarios for their future in EIA.

  14. Analysing connectivity with Granger causality and dynamic causal modelling.

    PubMed

    Friston, Karl; Moran, Rosalyn; Seth, Anil K

    2013-04-01

    This review considers state-of-the-art analyses of functional integration in neuronal macrocircuits. We focus on detecting and estimating directed connectivity in neuronal networks using Granger causality (GC) and dynamic causal modelling (DCM). These approaches are considered in the context of functional segregation and integration and--within functional integration--the distinction between functional and effective connectivity. We review recent developments that have enjoyed a rapid uptake in the discovery and quantification of functional brain architectures. GC and DCM have distinct and complementary ambitions that are usefully considered in relation to the detection of functional connectivity and the identification of models of effective connectivity. We highlight the basic ideas upon which they are grounded, provide a comparative evaluation and point to some outstanding issues.

  15. Unveiling causal activity of complex networks

    NASA Astrophysics Data System (ADS)

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

    2017-07-01

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

  16. 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

  17. Generalized Causal Mediation Analysis

    PubMed Central

    Albert, Jeffrey M.; Nelson, Suchitra

    2010-01-01

    Summary The goal of mediation analysis is to assess direct and indirect effects of a treatment or exposure on an outcome. More generally, we may be interested in the context of a causal model as characterized by a directed acyclic graph (DAG), where mediation via a specific path from exposure to outcome may involve an arbitrary number of links (or ‘stages’). Methods for estimating mediation (or pathway) effects are available for a continuous outcome and a continuous mediator related via a linear model, while for a categorical outcome or categorical mediator, methods are usually limited to two-stage mediation. We present a method applicable to multiple stages of mediation and mixed variable types using generalized linear models. We define pathway effects using a potential outcomes framework and present a general formula that provides the effect of exposure through any specified pathway. Some pathway effects are nonidentifiable and their estimation requires an assumption regarding the correlation between counterfactuals. We provide a sensitivity analysis to assess of the impact of this assumption. Confidence intervals for pathway effect estimates are obtained via a bootstrap method. The method is applied to a cohort study of dental caries in very low birth weight adolescents. A simulation study demonstrates low bias of pathway effect estimators and close-to-nominal coverage rates of confidence intervals. We also find low sensitivity to the counterfactual correlation in most scenarios. PMID:21306353

  18. Functional ability loss in sensory impaired and sensory unimpaired very old adults: analyzing causal relations with positive affect across four years.

    PubMed

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

    2014-11-01

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

  19. Obesity and infection: reciprocal causality.

    PubMed

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

    2015-01-01

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

  20. Causal localizations in relativistic quantum mechanics

    NASA Astrophysics Data System (ADS)

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

    2015-07-01

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

  1. Improving causal inferences in risk analysis.

    PubMed

    Cox, Louis Anthony Tony

    2013-10-01

    Recent headlines and scientific articles projecting significant human health benefits from changes in exposures too often depend on unvalidated subjective expert judgments and modeling assumptions, especially about the causal interpretation of statistical associations. Some of these assessments are demonstrably biased toward false positives and inflated effects estimates. More objective, data-driven methods of causal analysis are available to risk analysts. These can help to reduce bias and increase the credibility and realism of health effects risk assessments and causal claims. For example, quasi-experimental designs and analysis allow alternative (noncausal) explanations for associations to be tested, and refuted if appropriate. Panel data studies examine empirical relations between changes in hypothesized causes and effects. Intervention and change-point analyses identify effects (e.g., significant changes in health effects time series) and estimate their sizes. Granger causality tests, conditional independence tests, and counterfactual causality models test whether a hypothesized cause helps to predict its presumed effects, and quantify exposure-specific contributions to response rates in differently exposed groups, even in the presence of confounders. Causal graph models let causal mechanistic hypotheses be tested and refined using biomarker data. These methods can potentially revolutionize the study of exposure-induced health effects, helping to overcome pervasive false-positive biases and move the health risk assessment scientific community toward more accurate assessments of the impacts of exposures and interventions on public health.

  2. 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.

  3. Diagnosis and causal explanation in psychiatry.

    PubMed

    Maung, Hane Htut

    2016-12-01

    In clinical medicine, a diagnosis can offer an explanation of a patient's symptoms by specifying the pathology that is causing them. Diagnoses in psychiatry are also sometimes presented in clinical texts as if they pick out pathological processes that cause sets of symptoms. However, current evidence suggests the possibility that many diagnostic categories in psychiatry are highly causally heterogeneous. For example, major depressive disorder may not be associated with a single type of underlying pathological process, but with a range of different causal pathways, each involving complex interactions of various biological, psychological, and social factors. This paper explores the implications of causal heterogeneity for whether psychiatric diagnoses can be said to serve causal explanatory roles in clinical practice. I argue that while they may fall short of picking out a specific cause of the patient's symptoms, they can nonetheless supply different sorts of clinically relevant causal information. In particular, I suggest that some psychiatric diagnoses provide negative information that rules out certain causes, some provide approximate or disjunctive information about the range of possible causal processes, and some provide causal information about the relations between the symptoms themselves.

  4. 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.

  5. 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

  6. 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.

  7. 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

    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

    2015-08-11

    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. 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. 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). Waist and hip circumferences, and waist-hip ratio. 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. 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. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  8. 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

  9. 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

  10. 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.

  11. Causality, mediation and time: a dynamic viewpoint

    PubMed Central

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

    2012-01-01

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

  12. Causality, mediation and time: a dynamic viewpoint.

    PubMed

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

    2012-10-01

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

  13. 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

  14. Establishing causal coherence across sentences: an ERP study.

    PubMed

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

    2011-05-01

    This study examined neural activity associated with establishing causal relationships across sentences during on-line 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.

  15. More discussions for granger causality and new causality measures.

    PubMed

    Hu, Sanqing; Cao, Yu; Zhang, Jianhai; Kong, Wanzeng; Yang, Kun; Zhang, Yanbin; Li, Xun

    2012-02-01

    Granger causality (GC) has been widely applied in economics and neuroscience to reveal causality influence of time series. In our previous paper (Hu et al., in IEEE Trans on Neural Netw, 22(6), pp. 829-844, 2011), we proposed new causalities in time and frequency domains and particularly focused on new causality in frequency domain by pointing out the shortcomings/limitations of GC or Granger-alike causality metrics and the advantages of new causality. In this paper we continue our previous discussions and focus on new causality and GC or Granger-alike causality metrics in time domain. Although one strong motivation was introduced in our previous paper (Hu et al., in IEEE Trans on Neural Netw, 22(6), pp. 829-844, 2011) we here present additional motivation for the proposed new causality metric and restate the previous motivation for completeness. We point out one property of conditional GC in time domain and the shortcomings/limitations of conditional GC which cannot reveal the real strength of the directional causality among three time series. We also show the shortcomings/limitations of directed causality (DC) or normalize DC for multivariate time series and demonstrate it cannot reveal real causality at all. By calculating GC and new causality values for an example we demonstrate the influence of one of the time series on the other is linearly increased as the coupling strength is linearly increased. This fact further supports reasonability of new causality metric. We point out that larger instantaneous correlation does not necessarily mean larger true causality (e.g., GC and new causality), or vice versa. Finally we conduct analysis of statistical test for significance and asymptotic distribution property of new causality metric by illustrative examples.

  16. 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…

  17. 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…

  18. 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…

  19. 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…

  20. 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…

  1. 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…

  2. 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…

  3. Illness causal beliefs in Turkish immigrants.

    PubMed

    Minas, Harry; Klimidis, Steven; Tuncer, Can

    2007-07-24

    People hold a wide variety of beliefs concerning the causes of illness. Such beliefs vary across cultures and, among immigrants, may be influenced by many factors, including level of acculturation, gender, level of education, and experience of illness and treatment. This study examines illness causal beliefs in Turkish-immigrants in Australia. Causal beliefs about somatic and mental illness were examined in a sample of 444 members of the Turkish population of Melbourne. The socio-demographic characteristics of the sample were broadly similar to those of the Melbourne Turkish community. Five issues were examined: the structure of causal beliefs; the relative frequency of natural, supernatural and metaphysical beliefs; ascription of somatic, mental, or both somatic and mental conditions to the various causes; the correlations of belief types with socio-demographic, modernizing and acculturation variables; and the relationship between causal beliefs and current illness. Principal components analysis revealed two broad factors, accounting for 58 percent of the variation in scores on illness belief scales, distinctly interpretable as natural and supernatural beliefs. Second, beliefs in natural causes were more frequent than beliefs in supernatural causes. Third, some causal beliefs were commonly linked to both somatic and mental conditions while others were regarded as more specific to either somatic or mental disorders. Last, there was a range of correlations between endorsement of belief types and factors defining heterogeneity within the community, including with demographic factors, indicators of modernizing and acculturative processes, and the current presence of illness. Results supported the classification of causal beliefs proposed by Murdock, Wilson & Frederick, with a division into natural and supernatural causes. While belief in natural causes is more common, belief in supernatural causes persists despite modernizing and acculturative influences. Different

  4. 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

  5. Causal Reasoning In Diagnostic Expert Systems

    NASA Astrophysics Data System (ADS)

    Torasso, Pietro; Console, Luca

    1987-05-01

    In order to deal efficiently with difficult diagnostic problems, deep models (based on causal knowledge) have been adopted in some experimental diagnostic expert system. This paper describes a two levels architecture for a diagnostic expert system: CHECK (Combining HEuristic and Causal Knowledge). CHECK is based on the close interaction of two levels of knowledge representation, heuristic and causal respectively. In the heuristic (shallow) level knowledge is represented by means of a hybrid formalism combining at various levels frames and production rules; in the deep level knowledge is represented by means of causal networks in which (physical or physiological) states are connected via cause-effect relations. The two levels strictly cooperate in the diagnostic process, in particular the heuristic level is used to focus reasoning, generating diagnostic hypotheses to be refined, confirmed (disconfirmed) and explained by the deep level. Heuristic (surface) level knowledge is invoked first to generate diagnostic hypotheses. These hypotheses are then passed to the underlying level for a deep confirmation (so that they are used to focus reasoning in the causal network). If a hypothesis can be confirmed, a precise explanation is generated, unaccounted and/or unexpected data are taken into account and correlated hypotheses suggested. If a hypothesis is rejected, alternative hypotheses to be considered are suggested to the surface level. Deep level knowledge can be used also to provide general explanations about the causal model of the domain, independently from the data of a particular consultation. As an example for validating the architectural choices of CHECK we have implemented a version of it for diagnostic reasoning in the field of hepatology. Production rules, frames and causal networks are described by the knowledge engineer in a knowledge representation language we have designed and then coded, through the use of a preprocessing tool, in Prolog. Particular object

  6. Computer Use, Confidence, Attitudes, and Knowledge: A Causal Analysis.

    ERIC Educational Resources Information Center

    Levine, Tamar; Donitsa-Schmidt, Smadar

    1998-01-01

    Introduces a causal model which links measures of computer experience, computer-related attitudes, computer-related confidence, and perceived computer-based knowledge. The causal model suggests that computer use has a positive effect on perceived computer self-confidence, as well as on computer-related attitudes. Questionnaires were administered…

  7. Making valid causal inferences from observational data.

    PubMed

    Martin, Wayne

    2014-02-15

    The ability to make strong causal inferences, based on data derived from outside of the laboratory, is largely restricted to data arising from well-designed randomized control trials. Nonetheless, a number of methods have been developed to improve our ability to make valid causal inferences from data arising from observational studies. In this paper, I review concepts of causation as a background to counterfactual causal ideas; the latter ideas are central to much of current causal theory. Confounding greatly constrains causal inferences in all observational studies. Confounding is a biased measure of effect that results when one or more variables, that are both antecedent to the exposure and associated with the outcome, are differentially distributed between the exposed and non-exposed groups. Historically, the most common approach to control confounding has been multivariable modeling; however, the limitations of this approach are discussed. My suggestions for improving causal inferences include asking better questions (relates to counterfactual ideas and "thought" trials); improving study design through the use of forward projection; and using propensity scores to identify potential confounders and enhance exchangeability, prior to seeing the outcome data. If time-dependent confounders are present (as they are in many longitudinal studies), more-advanced methods such as marginal structural models need to be implemented. Tutorials and examples are cited where possible. Copyright © 2013 Elsevier B.V. All rights reserved.

  8. Causal Status and Coherence in Causal-Based Categorization

    ERIC Educational Resources Information Center

    Rehder, Bob; Kim, ShinWoo

    2010-01-01

    Research has documented two effects of interfeature causal knowledge on classification. A "causal status effect" occurs when features that are causes are more important to category membership than their effects. A "coherence effect" occurs when combinations of features that are consistent with causal laws provide additional…

  9. Causal Status and Coherence in Causal-Based Categorization

    ERIC Educational Resources Information Center

    Rehder, Bob; Kim, ShinWoo

    2010-01-01

    Research has documented two effects of interfeature causal knowledge on classification. A "causal status effect" occurs when features that are causes are more important to category membership than their effects. A "coherence effect" occurs when combinations of features that are consistent with causal laws provide additional…

  10. 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

  11. 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

    Baral, Hans-Otto; Bemmann, Martin

    2014-10-02

    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

  12. The role of causal models in analogical inference.

    PubMed

    Lee, Hee Seung; Holyoak, Keith J

    2008-09-01

    Computational models of analogy have assumed that the strength of an inductive inference about the target is based directly on similarity of the analogs and in particular on shared higher order relations. In contrast, work in philosophy of science suggests that analogical inference is also guided by causal models of the source and target. In 3 experiments, the authors explored the possibility that people may use causal models to assess the strength of analogical inferences. Experiments 1-2 showed that reducing analogical overlap by eliminating a shared causal relation (a preventive cause present in the source) from the target increased inductive strength even though it decreased similarity of the analogs. These findings were extended in Experiment 3 to cross-domain analogical inferences based on correspondences between higher order causal relations. Analogical inference appears to be mediated by building and then running a causal model. The implications of the present findings for theories of both analogy and causal inference are discussed.

  13. Causal Attributions in Young Children.

    ERIC Educational Resources Information Center

    Friedberg, Robert D.; Dalenberg, Constance J.

    1990-01-01

    Investigated the causal explanations children use to account for common experiences. In the study, 60 preschoolers watched videotaped puppet shows designed to elicit causal attributions. Most children predominantly used internal, unstable, and specific attributions. (CB)

  14. Causal Attributions in Young Children.

    ERIC Educational Resources Information Center

    Friedberg, Robert D.; Dalenberg, Constance J.

    1990-01-01

    Investigated the causal explanations children use to account for common experiences. In the study, 60 preschoolers watched videotaped puppet shows designed to elicit causal attributions. Most children predominantly used internal, unstable, and specific attributions. (CB)

  15. Genome-metabolite associations revealed low heritability, high genetic complexity, and causal relations for leaf metabolites in winter wheat (Triticum aestivum).

    PubMed

    Matros, Andrea; Liu, Guozheng; Hartmann, Anja; Jiang, Yong; Zhao, Yusheng; Wang, Huange; Ebmeyer, Erhard; Korzun, Viktor; Schachschneider, Ralf; Kazman, Ebrahim; Schacht, Johannes; Longin, Friedrich; Reif, Jochen Christoph; Mock, Hans-Peter

    2017-01-01

    We investigated associations between the metabolic phenotype, consisting of quantitative data of 76 metabolites from 135 contrasting winter wheat (Triticum aestivum) lines, and 17 372 single nucleotide polymorphism (SNP) markers. Metabolite profiles were generated from flag leaves of plants from three different environments, with average repeatabilities of 0.5-0.6. The average heritability of 0.25 was unaffected by the heading date. Correlations among metabolites reflected their functional grouping, highlighting the strict coordination of various routes of the citric acid cycle. Genome-wide association studies identified significant associations for six metabolic traits, namely oxalic acid, ornithine, L-arginine, pentose alcohol III, L-tyrosine, and a sugar oligomer (oligo II), with between one and 17 associated SNPs. Notable associations with genes regulating transcription or translation explained between 2.8% and 32.5% of the genotypic variance (pG). Further candidate genes comprised metabolite carriers (pG 32.5-38.1%), regulatory proteins (pG 0.3-11.1%), and metabolic enzymes (pG 2.5-32.5%). The combinatorial use of genomic and metabolic data to construct partially directed networks revealed causal inferences in the correlated metabolite traits and associated SNPs. The evaluated causal relationships will provide a basis for predicting the effects of genetic interferences on groups of correlated metabolic traits, and thus on specific metabolic phenotypes. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology.

  16. 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.…

  17. 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.…

  18. 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

  19. Causal responsibility and counterfactuals.

    PubMed

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

    2013-08-01

    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. Copyright © 2013 Cognitive Science Society, Inc.

  20. Do tachyons violate the causality principle?

    NASA Astrophysics Data System (ADS)

    Nibart, Gilles

    2000-05-01

    Very early, A. EINSTEIN has shown that particles with velocities greater than the velocity of light in vacuum may produce causal anomalies. Later, in quantum mechanics CPT transformations have allowed causal loops at a microscopic scale. So the possibility of faster-than-light particles has been analyzed again. The Meta-Relativity has extended the special theory of Relativity to particles beyond the light barrier (tachyons), by using the relativist formula with complex values. It has assigned to any tachyon an imaginary proper mass which does not easily offer a physical interpretation. In the framework of that theory, tachyons may appear to travel backwards in time and have negative energies, but they have to be interpreted as travelling forwards in time with positive energies (reinterpretation principle). The Meta-Relativity allows a tachyon reflection or re-emission to produce a causal loop, but some authors rejects the objection by postulating the tachyon emission cannot be systematically repeated. So causal loops can only occur at a microscopic scale. The theory of Relativity in the spacelike region has been developed by R. DUTHEIL using the tensor formalism of the general theory of Relativity. He defined tachyonic referential frames (TRF) with an other metric tensor and he showed it leads to an other LORENTZ group of transformations—the superluminal LORENTZ group. In this theory, tachyons always have a positive energy and a real proper mass, but their behavior must be described with tachyonic referential frames. R. DUTHEIL argued from the isomorphism of the both LORENTZ groups to prove the ZEEMAN'S theorem is respected by tachyons; so a sequence order is always preserved by any superluminal transformation. In the present communication, I show that time coordinates of tachyonic referential frames do not preserve causal order and do not make sense for natural observers. Nevertheless I show that the causal order is preserved within the superluminal proper time

  1. 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

  2. Implicit Causality, Implicit Consequentiality and Semantic Roles

    ERIC Educational Resources Information Center

    Crinean, Marcelle; Garnham, Alan

    2006-01-01

    Stewart, Pickering, and Sanford (1998) reported a new type of semantic inference, implicit consequentiality, which they suggest is comparable to, although not directly related to, the well-documented phenomenon of implicit causality. It is our contention that there is a direct relation between these two semantic phenomena but that this relation…

  3. Implicit Causality, Implicit Consequentiality and Semantic Roles

    ERIC Educational Resources Information Center

    Crinean, Marcelle; Garnham, Alan

    2006-01-01

    Stewart, Pickering, and Sanford (1998) reported a new type of semantic inference, implicit consequentiality, which they suggest is comparable to, although not directly related to, the well-documented phenomenon of implicit causality. It is our contention that there is a direct relation between these two semantic phenomena but that this relation…

  4. 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…

  5. 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…

  6. Inferring causal structure: a quantum advantage

    NASA Astrophysics Data System (ADS)

    Ried, Katja; Spekkens, Robert

    2014-03-01

    The problem of inferring causal relations from observed correlations is central to science, and extensive study has yielded both important conceptual insights and widely used practical applications. Yet some of the simplest questions are impossible to answer classically: for instance, if one observes correlations between two variables (such as taking a new medical treatment and the subject's recovery), does this show a direct causal influence, or is it due to some hidden common cause? We develop a framework for quantum causal inference, and show how quantum theory provides a unique advantage in this decision problem. The key insight is that certain quantum correlations can only arise from specific causal structures, whereas pairs of classical variables can exhibit any pattern of correlation regardless of whether they have a common cause or a direct-cause relation. For example, suppose one measures the same Pauli observable on two qubits. If they share a common cause, such as being prepared in an entangled state, then one never finds perfect (positive) correlations in every basis, whereas perfect anticorrelations are possible (if one prepares the singlet state). Conversely, if a channel connects the qubits, hence a direct causal influence, perfect anticorrelations are impossible.

  7. 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.

  8. Restrictions for the causal inferences in an interferometric system

    NASA Astrophysics Data System (ADS)

    Rossi, R.

    2017-07-01

    Causal discovery algorithms allow for the inference of causal structures from probabilistic relations of random variables. A natural field for the application of this tool is quantum mechanics, where a long-standing debate about the role of causality in the theory has flourished since its early days. In this paper, a causal discovery algorithm is applied in the search for causal models to describe a quantum version of Wheeler's delayed-choice experiment. The outputs explicitly show the restrictions for the introduction of classical concepts in this system. The exclusion of models with two hidden variables is one of them. A consequence of such a constraint is the impossibility to construct a causal model that avoids superluminal causation and assumes an objective view of the wave and particle properties simultaneously.

  9. Positive and negative implications of the causal illusion.

    PubMed

    Blanco, Fernando

    2017-04-01

    The human cognitive system is fine-tuned to detect patterns in the environment with the aim of predicting important outcomes and, eventually, to optimize behavior. Built under the logic of the least-costly mistake, this system has evolved biases to not overlook any meaningful pattern, even if this means that some false alarms will occur, as in the case of when we detect a causal link between two events that are actually unrelated (i.e., a causal illusion). In this review, we examine the positive and negative implications of causal illusions, emphasizing emotional aspects (i.e., causal illusions are negatively associated with negative mood and depression) and practical, health-related issues (i.e., causal illusions might underlie pseudoscientific beliefs, leading to dangerous decisions). Finally, we describe several ways to obtain control over causal illusions, so that we could be able to produce them when they are beneficial and avoid them when they are harmful.

  10. 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.

  11. 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.

  12. 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.

  13. Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables

    NASA Astrophysics Data System (ADS)

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

    2009-12-01

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

  14. Aging and retrospective revaluation of causal learning.

    PubMed

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

    2012-01-01

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

  15. Choice of Units and the Causal Markov Condition

    NASA Astrophysics Data System (ADS)

    Zhang, Jiji; Spirtes, Peter

    2014-03-01

    Elliott Sober's well-known challenge to the principle of the common cause -- and to its generalization, the causal Markov condition -- appeals to the apparent positive correlation between two causally unconnected quantities: Venetian sea levels and British bread prices. In this paper we examine Kevin Hoover's and Daniel Steel's opposite evaluations of Sober's case. We argue that the difference in their assessments results from a difference in their choice of units and populations for statistical modeling. Our analysis suggests yet another diagnosis of Sober's counterexample: the failure of the causal Markov condition in the population chosen by Sober and Steel is due to the presence of causal relations that hold between the relevant properties across units. Such inter-unit causation is left unrepresented in causal models congenial to statistical analysis, because statistics does not deal with inter-unit relationships once the units are fixed. Accordingly, the causal Markov condition is formulated in terms of causal structures that depict intra-unit causal relations only. It is therefore worth highlighting a methodological principle for causal inference: the units should be so chosen that they do not interfere with each other, a principle that, fortunately, is often observed in practice.

  16. Causal structure of the early universe

    NASA Astrophysics Data System (ADS)

    Gott, J. R., III

    Some fundamental problems regarding the standard big bang model are related to the universe's matter excess and the isotropy of the cosmic microwave background radiation. However, developments in particle physics point to answers to these problems. The formation of multiple bubble universes represents one interesting possiblity. An explanation for the universe's matter excess is now provided by Grand Unified Theories (GUTs), while models considering 'false vacuum' epochs can solve the isotropy problem by allowing more time for different regions to come into causal contact. Each of these models is discussed along with their causal structure.

  17. Causal events enter awareness faster than non-causal events

    PubMed Central

    Wagemans, Johan; de-Wit, Lee

    2017-01-01

    Philosophers have long argued that causality cannot be directly observed but requires a conscious inference (Hume, 1967). Albert Michotte however developed numerous visual phenomena in which people seemed to perceive causality akin to primary visual properties like colour or motion (Michotte, 1946). Michotte claimed that the perception of causality did not require a conscious, deliberate inference but, working over 70 years ago, he did not have access to the experimental methods to test this claim. Here we employ Continuous Flash Suppression (CFS)—an interocular suppression technique to render stimuli invisible (Tsuchiya & Koch, 2005)—to test whether causal events enter awareness faster than non-causal events. We presented observers with ‘causal’ and ‘non-causal’ events, and found consistent evidence that participants become aware of causal events more rapidly than non-causal events. Our results suggest that, whilst causality must be inferred from sensory evidence, this inference might be computed at low levels of perceptual processing, and does not depend on a deliberative conscious evaluation of the stimulus. This work therefore supports Michotte’s contention that, like colour or motion, causality is an immediate property of our perception of the world. PMID:28149698

  18. 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.

  19. Causal structure of general relativistic spacetimes

    SciTech Connect

    Howard, Ecaterina

    2010-06-15

    We present some of the recent results and open questions on the causality problem in General Relativity. The concept of singularity is intimately connected with future trapped surface and inner event horizon formation. We offer a brief overview of the Hawking-Penrose singularity theorems and discuss a few open problems concerning the future Cauchy development (domain of dependence), break-down criteria and energy conditions for the horizon stability. A key question is whether causality violating regions, generating a Cauchy horizon are allowed.We raise several questions concerning the invisibility and stability of closed trapped surfaces from future null infinity and derive the imprisonment conditions. We provide an up-to-date perspective of the causal boundaries and spacelike conformal boundary extensions for time oriented Lorentzian manifolds and more exotic settings.

  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. 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

  2. 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.

  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. Learning to Write History: The Role of Causality

    ERIC Educational Resources Information Center

    Coffin, Caroline

    2004-01-01

    Historians generally agree that causality is central to historical writing. The fact that many school history students have difficulty handling and expressing causal relations is therefore of concern. That is, whereas historians tend to favor impersonal, abstract structures as providing suitable explanations for historical events and states of…

  5. A Review of Children's Use of Causal Inference Principles.

    ERIC Educational Resources Information Center

    Sedlak, Andrea J.; Kurtz, Susan T.

    1981-01-01

    Examines cues which guide the discovery of simple cause-effect relations, beginning with the properties (suggested by Hume) of temporal precedence, covariation and contiguity; explores variables which can influence simple causal judgments; and discusses developmental evidence regarding inference principles associated with causal schemata.…

  6. 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…

  7. Competition Between Antecedent and Between Subsequent Stimuli in Causal Judgments

    ERIC Educational Resources Information Center

    Arcediano, Francisco; Matute, Helena; Escobar, Martha; Miller, Ralph R.

    2005-01-01

    In the analysis of stimulus competition in causal judgment, 4 variables have been frequently confounded with respect to the conditions necessary for stimuli to compete: causal status of the competing stimuli (causes vs. effects), temporal order of the competing stimuli (antecedent vs. subsequent) relative to the noncompeting stimulus,…

  8. 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…

  9. Feature Inference and the Causal Structure of Categories

    ERIC Educational Resources Information Center

    Rehder, B.; Burnett, R.C.

    2005-01-01

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

  10. 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…

  11. Causal Coherence in Deaf and Hearing Students' Written Narratives

    ERIC Educational Resources Information Center

    Arfe, Barbara; Boscolo, Pietro

    2006-01-01

    This study investigates the causal coherence of deaf students' written narratives and the relation between students' use of causal structures in narrative writing and their linguistic skills. The written narratives of 17 deaf high school students were compared with those of 2 groups of hearing writers: 17 high school students and 16 second…

  12. How Causal Knowledge Affects Classification: A Generative Theory of Categorization

    ERIC Educational Resources Information Center

    Rehder, Bob; Kim, ShinWoo

    2006-01-01

    Several theories have been proposed regarding how causal relations among features of objects affect how those objects are classified. The assumptions of these theories were tested in 3 experiments that manipulated the causal knowledge associated with novel categories. There were 3 results. The 1st was a multiple cause effect in which a feature's…

  13. 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…

  14. 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…

  15. Learning to Write History: The Role of Causality

    ERIC Educational Resources Information Center

    Coffin, Caroline

    2004-01-01

    Historians generally agree that causality is central to historical writing. The fact that many school history students have difficulty handling and expressing causal relations is therefore of concern. That is, whereas historians tend to favor impersonal, abstract structures as providing suitable explanations for historical events and states of…

  16. 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…

  17. 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…

  18. 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…

  19. Polish spaces of causal curves

    NASA Astrophysics Data System (ADS)

    Miller, Tomasz

    2017-06-01

    We propose and study a new approach to the topologization of spaces of (possibly not all) future-directed causal curves in a stably causal spacetime. It relies on parametrizing the curves ;in accordance; with a chosen time function. Thus obtained topological spaces of causal curves are separable and completely metrizable, i.e. Polish. The latter property renders them particularly useful in the optimal transport theory. To illustrate this fact, we explore the notion of a causal time-evolution of measures in globally hyperbolic spacetimes and discuss its physical interpretation.

  20. 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…

  1. 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…

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

    PubMed

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

    2013-05-01

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

  3. 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…

  4. 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…

  5. 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.

  6. 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.

  7. 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.

  8. Inductive reasoning about causally transmitted properties.

    PubMed

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

    2008-11-01

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

  9. 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.

  10. Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data.

    PubMed

    Chen, Yonghong; Bressler, Steven L; Ding, Mingzhou

    2006-01-30

    It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method.

  11. 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…

  12. 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…

  13. 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…

  14. Commentary on Causal Prescriptive Statements

    ERIC Educational Resources Information Center

    Graesser, Arthur C.; Hu, Xiangen

    2011-01-01

    Causal prescriptive statements are valued in the social sciences when there is the goal of helping people through interventions. The articles in this special issue cover different methods for testing causal prescriptive statements. This commentary identifies both virtues and liabilities of these different approaches. We argue that it is extremely…

  15. 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…

  16. Causality in Solving Economic Problems

    ERIC Educational Resources Information Center

    Robinson, A. Emanuel; Sloman, Steven A.; Hagmayer, York; Hertzog, Christopher K.

    2010-01-01

    The role of causal beliefs in people's decisions when faced with economic problems was investigated. Two experiments are reported that vary the causal structure in prisoner's dilemma-like economic situations. We measured willingness to cooperate or defect and collected justifications and think-aloud protocols to examine the strategies that people…

  17. 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…

  18. Causal Learning with Local Computations

    ERIC Educational Resources Information Center

    Fernbach, Philip M.; Sloman, Steven A.

    2009-01-01

    The authors proposed and tested a psychological theory of causal structure learning based on local computations. Local computations simplify complex learning problems via cues available on individual trials to update a single causal structure hypothesis. Structural inferences from local computations make minimal demands on memory, require…

  19. Commentary on Causal Prescriptive Statements

    ERIC Educational Resources Information Center

    Graesser, Arthur C.; Hu, Xiangen

    2011-01-01

    Causal prescriptive statements are valued in the social sciences when there is the goal of helping people through interventions. The articles in this special issue cover different methods for testing causal prescriptive statements. This commentary identifies both virtues and liabilities of these different approaches. We argue that it is extremely…

  20. 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…

  1. Causal Attributions of Shy Subjects.

    ERIC Educational Resources Information Center

    Teglasi, Hedwig; Hoffman, Mary Ann

    1982-01-01

    Causal attributions of shy students (N=36) were compared with those of a comparison group of students (N=36) in ten situations. Significant differences between the two groups emerged when explaining outcomes of situations considered to be problematic for shy individuals. Causal attributions may reflect realistic and situation-specific…

  2. Paradoxical Behavior of Granger Causality

    NASA Astrophysics Data System (ADS)

    Witt, Annette; Battaglia, Demian; Gail, Alexander

    2013-03-01

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

  3. Structure and Strength in Causal Induction

    ERIC Educational Resources Information Center

    Griffiths, Thomas L.; Tenenbaum, Joshua B.

    2005-01-01

    We present a framework for the rational analysis of elemental causal induction--learning about the existence of a relationship between a single cause and effect--based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship…

  4. 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

  5. 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.

  6. [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.

  7. 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

  8. 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.

  9. A quantum probability model of causal reasoning.

    PubMed

    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.

  10. Children use temporal cues to learn causal directionality.

    PubMed

    Rottman, Benjamin M; Kominsky, Jonathan F; Keil, Frank C

    2014-04-01

    The ability to learn the direction of causal relations is critical for understanding and acting in the world. We investigated how children learn causal directionality in situations in which the states of variables are temporally dependent (i.e., autocorrelated). In Experiment 1, children learned about causal direction by comparing the states of one variable before versus after an intervention on another variable. In Experiment 2, children reliably inferred causal directionality merely from observing how two variables change over time; they interpreted Y changing without a change in X as evidence that Y does not influence X. Both of these strategies make sense if one believes the variables to be temporally dependent. We discuss the implications of these results for interpreting previous findings. More broadly, given that many real-world environments are characterized by temporal dependency, these results suggest strategies that children may use to learn the causal structure of their environments.

  11. Translating context to causality in cardiovascular disparities research.

    PubMed

    Benn, Emma K T; Goldfeld, Keith S

    2016-04-01

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

  12. Principal stratification in causal inference.

    PubMed

    Frangakis, Constantine E; Rubin, Donald B

    2002-03-01

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

  13. Causal inference and developmental psychology.

    PubMed

    Foster, E Michael

    2010-11-01

    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 the risk factor actually causes outcomes. Random assignment is not possible in many instances, and for that reason, psychologists must rely on observational studies. Such studies identify associations, and causal interpretation of such associations requires additional assumptions. Research in developmental psychology generally has relied on various forms of linear regression, but this methodology has limitations for causal inference. Fortunately, methodological developments in various fields are providing new tools for causal inference-tools that rely on more plausible assumptions. This article describes the limitations of regression for causal inference and describes how new tools might offer better causal inference. This discussion highlights the importance of properly identifying covariates to include (and exclude) from the analysis. This discussion considers the directed acyclic graph for use in accomplishing this task. With the proper covariates having been chosen, many of the available methods rely on the assumption of "ignorability." The article discusses the meaning of ignorability and considers alternatives to this assumption, such as instrumental variables estimation. Finally, the article considers the use of the tools discussed in the context of a specific research question, the effect of family structure on child development.

  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. 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

  16. Causal attributions of dementia among Korean American immigrants.

    PubMed

    Lee, Sang E; Diwan, Sadhna; Yeo, Gwen

    2010-11-01

    To better understand conceptualizations of dementia, this study explored causal attributions of dementia among 209 Korean Americans, using a self-administered questionnaire in Korean. Findings show that Korean Americans endorsed various causal attributions. Factor analysis yielded 3 dimensions of their attributions including psychological, physical/environmental, and cognitive/social. Bivariate analyses showed that younger age and higher education were related to more physical/environmental attributions, and younger age was related to more cognitive/social attributions. The study provides an understanding of causal attributions of dementia that practitioners need to understand to provide culturally competent practice and highlights a need to customize public education messages by specific ethnic groups.

  17. 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…

  18. Measuring autonomy and emergence via Granger causality.

    PubMed

    Seth, Anil K

    2010-01-01

    Concepts of emergence and autonomy are central to artificial life and related cognitive and behavioral sciences. However, quantitative and easy-to-apply measures of these phenomena are mostly lacking. Here, I describe quantitative and practicable measures for both autonomy and emergence, based on the framework of multivariate autoregression and specifically Granger causality. G-autonomy measures the extent to which the knowing the past of a variable helps predict its future, as compared to predictions based on past states of external (environmental) variables. G-emergence measures the extent to which a process is both dependent upon and autonomous from its underlying causal factors. These measures are validated by application to agent-based models of predation (for autonomy) and flocking (for emergence). In the former, evolutionary adaptation enhances autonomy; the latter model illustrates not only emergence but also downward causation. I end with a discussion of relations among autonomy, emergence, and consciousness.

  19. A new spin on causality constraints

    NASA Astrophysics Data System (ADS)

    Hartman, Thomas; Jain, Sachin; Kundu, Sandipan

    2016-10-01

    Causality in a shockwave state is related to the analytic properties of a four-point correlation function. Extending recent results for scalar probes, we show that this constrains the couplings of the stress tensor to light spinning operators in conformal field theory, and interpret these constraints in terms of the interaction with null energy. For spin-1 and spin-2 conserved currents in four dimensions, the resulting inequalities are a subset of the Hofman-Maldacena conditions for positive energy deposition. It is well known that energy conditions in holographic theories are related to causality on the gravity side; our results make a connection on the CFT side, and extend it to non-holographic theories.

  20. Boundary terms for causal sets

    NASA Astrophysics Data System (ADS)

    Buck, Michel; Dowker, Fay; Jubb, Ian; Surya, Sumati

    2015-10-01

    We propose a family of boundary terms for the action of a causal set with a spacelike boundary. We show that in the continuum limit one recovers the Gibbons-Hawking-York boundary term in the mean. We also calculate the continuum limit of the mean causal set action for an Alexandrov interval in flat spacetime. We find that it is equal to the volume of the codimension-2 intersection of the two light-cone boundaries of the interval.

  1. [Causal analysis approaches in epidemiology].

    PubMed

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

    2014-02-01

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

  2. Phytophthora megakarya and P. palmivora, closely related causal agents of cacao black pod rot, underwent increases in genome sizes and gene numbers by different mechanisms

    USDA-ARS?s Scientific Manuscript database

    Phytophthora megakarya (Pmeg) and P. palmivora (Ppal) are closely related species causing black pod rot of cacao. While Ppal is a cosmopolitan plant pathogen, cacao is the only known host of importance for Pmeg. Pmeg is more virulent on cacao than Ppal. Therefore, we have sequenced both the Pmeg and...

  3. 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…

  4. 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…

  5. 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.

  6. An introduction to causal inference.

    PubMed

    Pearl, Judea

    2010-02-26

    This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation.

  7. Causal Learning in Gambling Disorder: Beyond the Illusion of Control.

    PubMed

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

    2017-06-01

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

  8. 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

  9. A Complex Systems Approach to Causal Discovery in Psychiatry.

    PubMed

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

    2016-01-01

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

  10. 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.

  11. [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.

  12. 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.

  13. 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

  14. 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.

  15. Temporal Information of Directed Causal Connectivity in Multi-Trial ERP Data using Partial Granger Causality.

    PubMed

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

    2016-01-01

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

  16. Imputation of adverse drug reactions: Causality assessment in hospitals.

    PubMed

    Varallo, Fabiana Rossi; Planeta, Cleopatra S; Herdeiro, Maria Teresa; Mastroianni, Patricia de Carvalho

    2017-01-01

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

  17. Imputation of adverse drug reactions: Causality assessment in hospitals

    PubMed Central

    Mastroianni, Patricia de Carvalho

    2017-01-01

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

  18. 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

  19. Reciprocity, passivity and causality in Willis materials.

    PubMed

    Muhlestein, Michael B; Sieck, Caleb F; Alù, Andrea; Haberman, Michael R

    2016-10-01

    Materials that require coupling between the stress-strain and momentum-velocity constitutive relations were first proposed by Willis (Willis 1981 Wave Motion3, 1-11. (doi:10.1016/0165-2125(81)90008-1)) and are now known as elastic materials of the Willis type, or simply Willis materials. As coupling between these two constitutive equations is a generalization of standard elastodynamic theory, restrictions on the physically admissible material properties for Willis materials should be similarly generalized. This paper derives restrictions imposed on the material properties of Willis materials when they are assumed to be reciprocal, passive and causal. Considerations of causality and low-order dispersion suggest an alternative formulation of the standard Willis equations. The alternative formulation provides improved insight into the subwavelength physical behaviour leading to Willis material properties and is amenable to time-domain analyses. Finally, the results initially obtained for a generally elastic material are specialized to the acoustic limit.

  20. Reciprocity, passivity and causality in Willis materials

    NASA Astrophysics Data System (ADS)

    Muhlestein, Michael B.; Sieck, Caleb F.; Alù, Andrea; Haberman, Michael R.

    2016-10-01

    Materials that require coupling between the stress-strain and momentum-velocity constitutive relations were first proposed by Willis (Willis 1981 Wave Motion 3, 1-11. (doi:10.1016/0165-2125(81)90008-1)) and are now known as elastic materials of the Willis type, or simply Willis materials. As coupling between these two constitutive equations is a generalization of standard elastodynamic theory, restrictions on the physically admissible material properties for Willis materials should be similarly generalized. This paper derives restrictions imposed on the material properties of Willis materials when they are assumed to be reciprocal, passive and causal. Considerations of causality and low-order dispersion suggest an alternative formulation of the standard Willis equations. The alternative formulation provides improved insight into the subwavelength physical behaviour leading to Willis material properties and is amenable to time-domain analyses. Finally, the results initially obtained for a generally elastic material are specialized to the acoustic limit.

  1. 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…

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. The psychological distance of memories: Examining causal relations with mood and self-esteem in young, middle-aged and older adults.

    PubMed

    Demiray, Burcu; Freund, Alexandra M

    2017-03-01

    Three studies examined the self-enhancement function of autobiographical memory (measured with subjective temporal distance of memories). Participants recalled a memory of an attained and a failed goal and rated the subjective distance between each memory and the present. Study 1 showed that young adults with higher self-esteem felt closer to memories of attained goals and farther from failure memories than those with lower self-esteem. In Study 2, young, middle-aged and older adults with higher self-esteem felt closer to success memories, whereas self-esteem was unrelated to the temporal distance of failure memories. In both studies, feeling closer to success memories (and far from failure) led to enhanced mood. In Study 3, state self-esteem was experimentally manipulated. The manipulation had no effect on young and older adults, but middle-aged adults whose self-esteem was decreased, felt closer to success memories than failure memories. Results are discussed in relation to the temporal self-appraisal theory.

  7. 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.

  8. Bayesian networks improve causal environmental ...

    EPA Pesticide Factsheets

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

  9. A study of problems encountered in Granger causality analysis from a neuroscience perspective.

    PubMed

    Stokes, Patrick A; Purdon, Patrick L

    2017-08-22

    Granger causality methods were developed to analyze the flow of information between time series. These methods have become more widely applied in neuroscience. Frequency-domain causality measures, such as those of Geweke, as well as multivariate methods, have particular appeal in neuroscience due to the prevalence of oscillatory phenomena and highly multivariate experimental recordings. Despite its widespread application in many fields, there are ongoing concerns regarding the applicability of Granger causality methods in neuroscience. When are these methods appropriate? How reliably do they recover the system structure underlying the observed data? What do frequency-domain causality measures tell us about the functional properties of oscillatory neural systems? In this paper, we analyze fundamental properties of Granger-Geweke (GG) causality, both computational and conceptual. Specifically, we show that (i) GG causality estimates can be either severely biased or of high variance, both leading to spurious results; (ii) even if estimated correctly, GG causality estimates alone are not interpretable without examining the component behaviors of the system model; and (iii) GG causality ignores critical components of a system's dynamics. Based on this analysis, we find that the notion of causality quantified is incompatible with the objectives of many neuroscience investigations, leading to highly counterintuitive and potentially misleading results. Through the analysis of these problems, we provide important conceptual clarification of GG causality, with implications for other related causality approaches and for the role of causality analyses in neuroscience as a whole.

  10. A study of problems encountered in Granger causality analysis from a neuroscience perspective

    PubMed Central

    Stokes, Patrick A.; Purdon, Patrick L.

    2017-01-01

    Granger causality methods were developed to analyze the flow of information between time series. These methods have become more widely applied in neuroscience. Frequency-domain causality measures, such as those of Geweke, as well as multivariate methods, have particular appeal in neuroscience due to the prevalence of oscillatory phenomena and highly multivariate experimental recordings. Despite its widespread application in many fields, there are ongoing concerns regarding the applicability of Granger causality methods in neuroscience. When are these methods appropriate? How reliably do they recover the system structure underlying the observed data? What do frequency-domain causality measures tell us about the functional properties of oscillatory neural systems? In this paper, we analyze fundamental properties of Granger–Geweke (GG) causality, both computational and conceptual. Specifically, we show that (i) GG causality estimates can be either severely biased or of high variance, both leading to spurious results; (ii) even if estimated correctly, GG causality estimates alone are not interpretable without examining the component behaviors of the system model; and (iii) GG causality ignores critical components of a system’s dynamics. Based on this analysis, we find that the notion of causality quantified is incompatible with the objectives of many neuroscience investigations, leading to highly counterintuitive and potentially misleading results. Through the analysis of these problems, we provide important conceptual clarification of GG causality, with implications for other related causality approaches and for the role of causality analyses in neuroscience as a whole. PMID:28778996

  11. Causality in noncommutative two-sheeted space-times

    NASA Astrophysics Data System (ADS)

    Franco, Nicolas; Eckstein, Michał

    2015-10-01

    We investigate the causal structure of two-sheeted space-times using the tools of Lorentzian spectral triples. We show that the noncommutative geometry of these spaces allows for causal relations between the two sheets. The computation is given in detail when the sheet is a 2- or 4-dimensional globally hyperbolic spin manifold. The conclusions are then generalised to a point-dependent distance between the two sheets resulting from the fluctuations of the Dirac operator.

  12. 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

  13. 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

    2016-03-04

    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.

  14. Effect of a community-based diabetes self-management empowerment program on mental health-related quality of life: a causal mediation analysis from a randomized controlled trial.

    PubMed

    Sugiyama, Takehiro; Steers, William Neil; Wenger, Neil S; Duru, Obidiugwu Kenrik; Mangione, Carol M

    2015-03-22

    There is a paucity of evidence supporting the effectiveness of diabetes self-management education (DSME) in improving mental health-related quality of life (HRQoL) for African American and Latinos. Also, among studies supporting the favorable effects of DSME on mental HRQoL, the direct effect of DSME that is independent of improved glycemic control has never been investigated. The objectives of this study were to investigate the effect of community-based DSME intervention targeting empowerment on mental HRQoL and to determine whether the effect is direct or mediated by glycemic control. We conducted secondary analyses of data from the Diabetes Self-Care Study, a randomized controlled trial of a community-based DSME intervention. Study participants (n = 516) were African Americans and Latinos 55 years or older with poorly controlled diabetes (HbA1c ≥ 8.0%) recruited from senior centers and churches in Los Angeles. The intervention group received six weekly small-group self-care sessions based on the empowerment model. The control group received six lectures on unrelated geriatrics topics. The primary outcome variable in this secondary analysis was the change in Mental Component Summary score (MCS-12) from the SF-12 Health Survey between baseline and six-month follow-up. We used the change in HbA1c during the study period as the main mediator of interest in our causal mediation analysis. Additionally, possible mediations via social support and perceived empowerment attributable to the program were examined. MCS-12 increased by 1.4 points on average in the intervention group and decreased by 0.2 points in the control group (difference-in-change: 1.6 points, 95% CI: 0.1 to 3.2). In the causal mediation analysis, the intervention had a direct effect on MCS-12 improvement (1.7 points, 95% CI: 0.2 to 3.2) with no indirect effects mediated via HbA1c change (-0.1 points, 95% CI: -0.4 to 0.1), social support (0.1 points), and perception of empowerment (0.1 points

  15. Causal reasoning with mental models.

    PubMed

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

    2014-01-01

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

  16. 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

  17. Wormholes, baby universes, and causality

    SciTech Connect

    Visser, M. )

    1990-02-15

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

  18. Wormholes, baby universes, and causality

    NASA Astrophysics Data System (ADS)

    Visser, Matt

    1990-02-01

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

  19. Matched designs and causal diagrams

    PubMed Central

    Mansournia, Mohammad A; Hernán, Miguel A; Greenland, Sander

    2013-01-01

    We use causal diagrams to illustrate the consequences of matching and the appropriate handling of matched variables in cohort and case-control studies. The matching process generally forces certain variables to be independent despite their being connected in the causal diagram, a phenomenon known as unfaithfulness. We show how causal diagrams can be used to visualize many previous results about matched studies. Cohort matching can prevent confounding by the matched variables, but censoring or other missing data and further adjustment may necessitate control of matching variables. Case-control matching generally does not prevent confounding by the matched variables, and control of matching variables may be necessary even if those were not confounders initially. Matching on variables that are affected by the exposure and the outcome, or intermediates between the exposure and the outcome, will ordinarily produce irremediable bias. PMID:23918854

  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. Information thermodynamics on causal networks.

    PubMed

    Ito, Sosuke; Sagawa, Takahiro

    2013-11-01

    We study nonequilibrium thermodynamics of complex information flows induced by interactions between multiple fluctuating systems. Characterizing nonequilibrium dynamics by causal networks (i.e., Bayesian networks), we obtain novel generalizations of the second law of thermodynamics and the fluctuation theorem, which include an informational quantity characterized by the topology of the causal network. Our result implies that the entropy production in a single system in the presence of multiple other systems is bounded by the information flow between these systems. We demonstrate our general result by a simple model of biochemical adaptation.

  2. 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

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

    PubMed

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

    2017-08-08

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

  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. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

  5. 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

  6. How to establish causality in epilepsy surgery

    PubMed Central

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

    2013-01-01

    Focality in electro-clinical or neuroimaging data often motivates epileptologists to consider epilepsy surgery in patients with medically-uncontrolled seizures, while not all focal findings are causally associated with the generation of epileptic seizures. With the help of Hill's criteria, we have discussed how to establish causality in the context of the presurgical evaluation of epilepsy. The strengths of EEG include the ability to determine the temporal relationship between cerebral activities and clinical events; thus, scalp video-EEG is necessary in the evaluation of the majority of surgical candidates. The presence of associated ictal discharges can confirm the epileptic nature of a particular spell and whether an observed neuroimaging abnormality is causally associated with the epileptic seizure. Conversely, one should be aware that scalp EEG has a limited spatial resolution and sometimes exhibits propagated epileptiform discharges more predominantly than in situ discharges generated at the seizure-onset zone. Intraoperative or extraoperative electrocorticography (ECoG) is utilized when noninvasive presurgical evaluation, including anatomical and functional neuroimaging, fails to determine the margin between the presumed epileptogenic zone and eloquent cortex. Retrospective as well as prospective studies have reported that complete resection of the seizure-onset zone on ECoG was associated with a better seizure outcome, but not all patients became seizure-free following such resective surgery. Some retrospective studies suggested that resection of sites showing high-frequency oscillations (HFOs) at >80 Hz on interictal or ictal ECoG was associated with a better seizure outcome. Others reported that functionally-important areas may generate HFOs of a physiological nature during rest as well as sensorimotor and cognitive tasks. Resection of sites showing task-related augmentation of HFOs has been reported to indeed result in functional loss following surgery

  7. Causal discovery from medical textual data.

    PubMed Central

    Mani, S.; Cooper, G. F.

    2000-01-01

    Medical records usually incorporate investigative reports, historical notes, patient encounters or discharge summaries as textual data. This study focused on learning causal relationships from intensive care unit (ICU) discharge summaries of 1611 patients. Identification of the causal factors of clinical conditions and outcomes can help us formulate better management, prevention and control strategies for the improvement of health care. For causal discovery we applied the Local Causal Discovery (LCD) algorithm, which uses the framework of causal Bayesian Networks to represent causal relationships among model variables. LCD takes as input a dataset and outputs causes of the form variable Y causally influences variable Z. Using the words that occur in the discharge summaries as attributes for input, LCD output 8 purported causal relationships. The relationships ranked as most probable subjectively appear to be most causally plausible. PMID:11079942

  8. Causal discovery from medical textual data.

    PubMed

    Mani, S; Cooper, G F

    2000-01-01

    Medical records usually incorporate investigative reports, historical notes, patient encounters or discharge summaries as textual data. This study focused on learning causal relationships from intensive care unit (ICU) discharge summaries of 1611 patients. Identification of the causal factors of clinical conditions and outcomes can help us formulate better management, prevention and control strategies for the improvement of health care. For causal discovery we applied the Local Causal Discovery (LCD) algorithm, which uses the framework of causal Bayesian Networks to represent causal relationships among model variables. LCD takes as input a dataset and outputs causes of the form variable Y causally influences variable Z. Using the words that occur in the discharge summaries as attributes for input, LCD output 8 purported causal relationships. The relationships ranked as most probable subjectively appear to be most causally plausible.

  9. Causal learning is collaborative: Examining explanation and exploration in social contexts.

    PubMed

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

    2017-07-25

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

  10. An efficient two-tier causal protocol for mobile distributed systems.

    PubMed

    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.

  11. 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.

  12. 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.

  13. 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.

  14. Causal Categories: Relativistically Interacting Processes

    NASA Astrophysics Data System (ADS)

    Coecke, Bob; Lal, Raymond

    2013-04-01

    A symmetric monoidal category naturally arises as the mathematical structure that organizes physical systems, processes, and composition thereof, both sequentially and in parallel. This structure admits a purely graphical calculus. This paper is concerned with the encoding of a fixed causal structure within a symmetric monoidal category: causal dependencies will correspond to topological connectedness in the graphical language. We show that correlations, either classical or quantum, force terminality of the tensor unit. We also show that well-definedness of the concept of a global state forces the monoidal product to be only partially defined, which in turn results in a relativistic covariance theorem. Except for these assumptions, at no stage do we assume anything more than purely compositional symmetric-monoidal categorical structure. We cast these two structural results in terms of a mathematical entity, which we call a causal category. We provide methods of constructing causal categories, and we study the consequences of these methods for the general framework of categorical quantum mechanics.

  15. Hypothesizing and Refining Causal Models,

    DTIC Science & Technology

    1984-12-01

    the purposes of this research, it was critica ! to be able to represent a sequence of events, in which the learning program would look for causal... tlc sense because tliv imply random behavior. This is an oversimplified, but usc^ul telcological assumption about the nature of dependences in designed

  16. 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.

  17. Causal thinking and causal language in epidemiology: it's in the details

    PubMed Central

    Lipton, Robert; Ødegaard, Terje

    2005-01-01

    Although epidemiology is necessarily involved with elucidating causal processes, we argue that there is little practical need, having described an epidemiological result, to then explicitly label it as causal (or not). Doing so is a convention which obscures the valuable core work of epidemiology as an important constituent of public health practice. We discuss another approach which emphasizes the public health "use value" of research findings in regard to prediction and intervention independent from explicit metaphysical causal claims. Examples are drawn from smoking and lung cancer, with particular focus on the original 1964 Surgeon General's report on smoking and the new version released in 2004. The intent is to help the epidemiologist focus on the pertinent implications of research, which, from a public health point of view, in large part entails the ability to predict and to intervene. Further discussion will center on the importance of differentiating between technical/practical uses of causal language, as might be used in structural equations or marginal structural modeling, and more foundational notions of cause. We show that statistical/epidemiological results, such as "smoking two packs a day increases risk of lung cancer by 10 times" are in themselves a kind of causal argument that are not in need of additional support from relatively ambiguous language such as "smoking causes lung cancer." We will show that the confusion stemming from the use of this latter statement is more than mere semantics. Our goal is to allow researchers to feel more confident in the power of their research to tell a convincing story without resorting to metaphysical/unsupportable notions of cause. PMID:16053522

  18. Causal inference in obesity research.

    PubMed

    Franks, P W; Atabaki-Pasdar, N

    2017-03-01

    Obesity is a risk factor for a plethora of severe morbidities and premature death. Most supporting evidence comes from observational studies that are prone to chance, bias and confounding. Even data on the protective effects of weight loss from randomized controlled trials will be susceptible to confounding and bias if treatment assignment cannot be masked, which is usually the case with lifestyle and surgical interventions. Thus, whilst obesity is widely considered the major modifiable risk factor for many chronic diseases, its causes and consequences are often difficult to determine. Addressing this is important, as the prevention and treatment of any disease requires that interventions focus on causal risk factors. Disease prediction, although not dependent on knowing the causes, is nevertheless enhanced by such knowledge. Here, we provide an overview of some of the barriers to causal inference in obesity research and discuss analytical approaches, such as Mendelian randomization, that can help to overcome these obstacles. In a systematic review of the literature in this field, we found: (i) probable causal relationships between adiposity and bone health/disease, cancers (colorectal, lung and kidney cancers), cardiometabolic traits (blood pressure, fasting insulin, inflammatory markers and lipids), uric acid concentrations, coronary heart disease and venous thrombosis (in the presence of pulmonary embolism), (ii) possible causal relationships between adiposity and gray matter volume, depression and common mental disorders, oesophageal cancer, macroalbuminuria, end-stage renal disease, diabetic kidney disease, nuclear cataract and gall stone disease, and (iii) no evidence for causal relationships between adiposity and Alzheimer's disease, pancreatic cancer, venous thrombosis (in the absence of pulmonary embolism), liver function and periodontitis. © 2016 The Association for the Publication of the Journal of Internal Medicine.

  19. Suspected Greater Celandine hepatotoxicity: liver-specific causality evaluation of published case reports from Europe.

    PubMed

    Teschke, Rolf; Glass, Xaver; Schulze, Johannes; Eickhoff, Axel

    2012-03-01

    In 21 published case reports, the use of the herb Greater Celandine (GC) (Chelidonium majus L.) has been causally related to liver injury, but a variety of confounding variables were evident that might have offset causality. This study reanalyses causality levels in these cases with a liver-specific causality evaluation method. All 21 cases were submitted to the liver-specific, standardized, structured, quantitative and updated scale of the Council for International Organizations of Medical Sciences. This scale considers, among other items, latency period, course of alanine aminotransferase after treatment discontinuation, risk factors, comedication and alternative causes. Using this method for assessment, causality for GC was highly probable in two and probable in six cases, with lower causality grading in the remaining 13 cases. In these patients, causality for GC was possible in 10 cases and excluded in three cases. On the basis of the eight cases with highly probable and probable causality gradings, GC hepatotoxicity represents an idiosyncratic reaction of the metabolic type, whereas immunologic or obligatory hepatotoxic features are lacking. In some cases, alternative diagnoses and poor data quality were confounding variables that reduced causality levels. Confounding variables reduced causality levels for GC in reported cases of liver injury, but there is still striking evidence for herb-induced liver injury by GC with high causality gradings. GC hepatotoxicity is caused by an idiosyncratic reaction of the metabolic form, but there is uncertainty with respect to its culprit(s).

  20. Discrete group actions on spacetimes: causality conditions and the causal boundary

    NASA Astrophysics Data System (ADS)

    Harris, Steven G.

    2004-02-01

    Suppose a spacetime M is a quotient of a spacetime V by a discrete group of isometries. It is shown how causality conditions in the two spacetimes are related, and how one can learn about the future causal boundary on M by studying structures in V. The relations between the two are particularly simple (the boundary of the quotient is the quotient of the boundary) if both V and M have spacelike future boundaries and if it is known that the quotient of the future completion of V is past-distinguishing. (That last assumption is automatic in the case of M being multi-warped.) The author thanks the referees for helpful suggestions.

  1. Increasing fMRI sampling rate improves Granger causality estimates.

    PubMed

    Lin, Fa-Hsuan; Ahveninen, Jyrki; Raij, Tommi; Witzel, Thomas; Chu, Ying-Hua; Jääskeläinen, Iiro P; Tsai, Kevin Wen-Kai; Kuo, Wen-Jui; Belliveau, John W

    2014-01-01

    Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magnetic resonance imaging (fMRI) is increased 20-fold. Specifically, healthy volunteers performed a simple visuomotor task during blood oxygenation level dependent (BOLD) contrast based whole-head inverse imaging (InI). Granger causality analysis based on raw InI BOLD data sampled at 100-ms resolution detected the expected causal relations, whereas when the data were downsampled to the temporal resolution of 2 s typically used in echo-planar fMRI, the causality could not be detected. An additional control analysis, in which we SINC interpolated additional data points to the downsampled time series at 0.1-s intervals, confirmed that the improvements achieved with the real InI data were not explainable by the increased time-series length alone. We therefore conclude that the high-temporal resolution of InI improves the Granger causality connectivity analysis of the human brain.

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

    PubMed

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

    2008-07-15

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

  3. Whose statistical reasoning is facilitated by a causal structure intervention?

    PubMed

    McNair, Simon; Feeney, Aidan

    2015-02-01

    People often struggle when making Bayesian probabilistic estimates on the basis of competing sources of statistical evidence. Recently, Krynski and Tenenbaum (Journal of Experimental Psychology: General, 136, 430-450, 2007) proposed that a causal Bayesian framework accounts for peoples' errors in Bayesian reasoning and showed that, by clarifying the causal relations among the pieces of evidence, judgments on a classic statistical reasoning problem could be significantly improved. We aimed to understand whose statistical reasoning is facilitated by the causal structure intervention. In Experiment 1, although we observed causal facilitation effects overall, the effect was confined to participants high in numeracy. We did not find an overall facilitation effect in Experiment 2 but did replicate the earlier interaction between numerical ability and the presence or absence of causal content. This effect held when we controlled for general cognitive ability and thinking disposition. Our results suggest that clarifying causal structure facilitates Bayesian judgments, but only for participants with sufficient understanding of basic concepts in probability and statistics.

  4. The shared causal pasts and futures of cosmological events

    NASA Astrophysics Data System (ADS)

    Friedman, Andrew S.; Kaiser, David I.; Gallicchio, Jason

    2013-08-01

    We derive criteria for whether two cosmological events can have a shared causal past or a shared causal future, assuming a Friedmann-Lemaitre-Robertson-Walker (FLRW) universe with best-fit cosmological parameters from the Planck satellite. We further derive criteria for whether either cosmic event could have been in past causal contact with our own worldline since the time of the hot “big bang,” which we take to be the end of early-universe inflation. We find that pairs of objects such as quasars on opposite sides of the sky with redshifts z≥3.65 have no shared causal past with each other or with our past worldline. More complicated constraints apply if the objects are at different redshifts from each other or appear at some relative angle less than 180°, as seen from Earth. We present examples of observed quasar pairs that satisfy all, some, or none of the criteria for past causal independence. Given dark energy and the recent accelerated expansion, our observable Universe has a finite conformal lifetime, and hence a cosmic event horizon at current redshift z=1.87. We thus constrain whether pairs of cosmic events can signal each other’s worldlines before the end of time. Lastly, we generalize the criteria for shared past and future causal domains for FLRW universes with nonzero spatial curvature.

  5. Causal analysis of the viscoelastic Lamb problem.

    PubMed

    Moura, André

    2010-03-01

    A mathematical development is given for the generation of viscoelastic waves by an impulsive line source acting on the interface of a viscoelastic half space, where the viscoelasticity is characterized by two relaxation processes. The considered idealized viscoelastic medium is isotropic and characterized by two Lame constants appropriate for low frequencies, by their increments associated with the shift from low to high frequencies, and by separate relation times associated with each of the Lame constants. A causal solution is developed using integral transforms and an extension of Cagniard's method.

  6. Relativistic causality and position space renormalization

    NASA Astrophysics Data System (ADS)

    Todorov, Ivan

    2016-11-01

    The paper gives a historical survey of the causal position space renormalization with a special attention to the role of Raymond Stora in the development of this subject. Renormalization is reduced to subtracting the pole term in analytically regularized primitively divergent Feynman amplitudes. The identification of residues with "quantum periods" and their relation to recent developments in number theory are emphasized. We demonstrate the possibility of integration over internal vertices (that requires control over the infrared behavior) in the case of the massless φ4 theory and display the dilation and the conformal anomaly.

  7. 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

  8. Causal illusions in children when the outcome is frequent

    PubMed Central

    2017-01-01

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

  9. Compact Representations of Extended Causal Models

    DTIC Science & Technology

    2012-10-01

    models. A number of authors have suggested that an adequate account of actual causation must appeal not only to causal structure , but also to...about both causal structure and normality. Extended causal models are potentially very complex. In this paper, we show how it is possible to achieve...causation must be sensitive to considerations of normality, as well as to causal structure . In (Halpern & Hitchcock, 2011), we suggest a way of

  10. Causality Analysis of Neural Connectivity: Critical Examination of Existing Methods and Advances of New Methods

    PubMed Central

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

    2012-01-01

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

  11. Causality analysis of neural connectivity: critical examination of existing methods and advances of new methods.

    PubMed

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

    2011-06-01

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

  12. Emergent Geometry from Entropy and Causality

    NASA Astrophysics Data System (ADS)

    Engelhardt, Netta

    In this thesis, we investigate the connections between the geometry of spacetime and aspects of quantum field theory such as entanglement entropy and causality. This work is motivated by the idea that spacetime geometry is an emergent phenomenon in quantum gravity, and that the physics responsible for this emergence is fundamental to quantum field theory. Part I of this thesis is focused on the interplay between spacetime and entropy, with a special emphasis on entropy due to entanglement. In general spacetimes, there exist locally-defined surfaces sensitive to the geometry that may act as local black hole boundaries or cosmological horizons; these surfaces, known as holographic screens, are argued to have a connection with the second law of thermodynamics. Holographic screens obey an area law, suggestive of an association with entropy; they are also distinguished surfaces from the perspective of the covariant entropy bound, a bound on the total entropy of a slice of the spacetime. This construction is shown to be quite general, and is formulated in both classical and perturbatively quantum theories of gravity. The remainder of Part I uses the Anti-de Sitter/ Conformal Field Theory (AdS/CFT) correspondence to both expand and constrain the connection between entanglement entropy and geometry. The AdS/CFT correspondence posits an equivalence between string theory in the "bulk" with AdS boundary conditions and certain quantum field theories. In the limit where the string theory is simply classical General Relativity, the Ryu-Takayanagi and more generally, the Hubeny-Rangamani-Takayanagi (HRT) formulae provide a way of relating the geometry of surfaces to entanglement entropy. A first-order bulk quantum correction to HRT was derived by Faulkner, Lewkowycz and Maldacena. This formula is generalized to include perturbative quantum corrections in the bulk at any (finite) order. Hurdles to spacetime emergence from entanglement entropy as described by HRT and its quantum

  13. 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…

  14. 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…

  15. 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…

  16. 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.…

  17. 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…

  18. 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…

  19. Causal inference in economics and marketing.

    PubMed

    Varian, Hal R

    2016-07-05

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

  20. 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…

  1. 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

  2. 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…

  3. 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…

  4. An Introduction to Causal Inference

    DTIC Science & Technology

    2009-11-02

    University of California, Los Angeles,Computer Science Department,Los Angeles,CA,90095-1596 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING...University of California, Los Angeles Computer Science Department Los Angeles, CA, 90095-1596, USA judea@cs.ucla.edu September 30, 2009 Abstract This paper...Introduction The questions that motivate most studies in the health, social and behavioral sciences are not associational but causal in nature. For example

  5. Two roads to noncommutative causality

    NASA Astrophysics Data System (ADS)

    Besnard, Fabien

    2015-08-01

    We review the physical motivations and the mathematical results obtained so far in the isocone-based approach to noncommutative causality. We also give a briefer account of the alternative framework of Franco and Eckstein which is based on Lorentzian spectral triples. We compare the two theories on the simple example of the product geometry of the Minkowski plane by the finite noncommutative space with algebra M2(C).

  6. THE CAUSAL ANALYSIS / DIAGNOSIS DECISION ...

    EPA Pesticide Factsheets

    CADDIS is an on-line decision support system that helps investigators in the regions, states and tribes find, access, organize, use and share information to produce causal evaluations in aquatic systems. It is based on the US EPA's Stressor Identification process which is a formal method for identifying causes of impairments in aquatic systems. CADDIS 2007 increases access to relevant information useful for causal analysis and provides methods and tools that practitioners can use to analyze their own data. The new Candidate Cause section provides overviews of commonly encountered causes of impairments to aquatic systems: metals, sediments, nutrients, flow alteration, temperature, ionic strength, and low dissolved oxygen. CADDIS includes new Conceptual Models that illustrate the relationships from sources to stressors to biological effects. An Interactive Conceptual Model for phosphorus links the diagram with supporting literature citations. The new Analyzing Data section helps practitioners analyze their data sets and interpret and use those results as evidence within the USEPA causal assessment process. Downloadable tools include a graphical user interface statistical package (CADStat), and programs for use with the freeware R statistical package, and a Microsoft Excel template. These tools can be used to quantify associations between causes and biological impairments using innovative methods such as species-sensitivity distributions, biological inferenc

  7. 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.

  8. THE CAUSAL ANALYSIS / DIAGNOSIS DECISION ...

    EPA Pesticide Factsheets

    CADDIS is an on-line decision support system that helps investigators in the regions, states and tribes find, access, organize, use and share information to produce causal evaluations in aquatic systems. It is based on the US EPA's Stressor Identification process which is a formal method for identifying causes of impairments in aquatic systems. CADDIS 2007 increases access to relevant information useful for causal analysis and provides methods and tools that practitioners can use to analyze their own data. The new Candidate Cause section provides overviews of commonly encountered causes of impairments to aquatic systems: metals, sediments, nutrients, flow alteration, temperature, ionic strength, and low dissolved oxygen. CADDIS includes new Conceptual Models that illustrate the relationships from sources to stressors to biological effects. An Interactive Conceptual Model for phosphorus links the diagram with supporting literature citations. The new Analyzing Data section helps practitioners analyze their data sets and interpret and use those results as evidence within the USEPA causal assessment process. Downloadable tools include a graphical user interface statistical package (CADStat), and programs for use with the freeware R statistical package, and a Microsoft Excel template. These tools can be used to quantify associations between causes and biological impairments using innovative methods such as species-sensitivity distributions, biological inferenc

  9. 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…

  10. Processing Causality in Narrative Events: Temporal Order Matters

    ERIC Educational Resources Information Center

    Briner, Stephen W.; Virtue, Sandra; Kurby, Christopher A.

    2012-01-01

    To successfully comprehend narrative text, readers often make inferences about different causes and effects that occur in a text. In this study, participants read texts in which events related to a cause were presented before an effect (i.e., the forward causal condition), texts in which an effect was presented before the events related to a cause…

  11. Causality Assessment in Pharmacovigilance: Still a Challenge.

    PubMed

    Ralph Edwards, I

    2017-02-28

    Causality in pharmacovigilance is a difficult and time consuming exercise. This paper presents the challenges in determining causation by drug therapy. The first is that causation is complex and needs to be viewed from the context of the patient treated, rather than the drug product. Multiple causal vectors should be considered if we are to tackle the many issues involved in, for example, medication error and the many other factors that lead to bad outcomes from therapy, including failure to recognise known risk factors. The aim of pharmacovigilance is not only a bureaucratic exercise in public health norms, but is mainly concerned with small minorities of statistical outliers-and even individuals-whose experiences from harms may together form messages about causation that will prevent further at-risk patients from exposure, or at least assist with earlier recognition of drug-related harm and better management of such harm. This requires more time, more data, more analysis and more patient and clinical involvement in reporting useful clinical detail. The paradigm shift back towards gathering more case data relating to possible causation can be selective and would not be just retrogressive, nor necessarily too costly. Greater transparency of hypotheses and availability of anonymised case data will enrol more expertise into evaluations and hypothesis testing, and the provision of more complete and useful information should reduce clinical burdens from bad patient outcomes as well as their overall costs to society.

  12. Cognition and causality, fiction and explanation.

    PubMed

    Spaulding, W D

    1995-09-01

    The debate about the causal efficacy of cognition involves two overlapping but different issues: (1) whether explanatory fictions improve upon the power and utility of nonfictional explanations of behavior, and (2) whether any explanation, either purely empirical or purely inferential, can describe proximal causality in behavioral functioning. The resolution of the first issue depends on the purpose to which the explanation is to be put. The resolution of the second issue depends on the larger paradigmatic context in which causality is understood. In modern biosystemic models of behavior, linear causality is important only as a special case of the multidirectional and reciprocal causality which characterizes complex self-regulating systems.

  13. Identification of marginal causal relationships in gene networks from observational and interventional expression data

    PubMed Central

    Monneret, Gilles; Jaffrézic, Florence; Rau, Andrea; Zerjal, Tatiana; Nuel, Grégory

    2017-01-01

    Causal network inference is an important methodological challenge in biology as well as other areas of application. Although several causal network inference methods have been proposed in recent years, they are typically applicable for only a small number of genes, due to the large number of parameters to be estimated and the limited number of biological replicates available. In this work, we consider the specific case of transcriptomic studies made up of both observational and interventional data in which a single gene of biological interest is knocked out. We focus on a marginal causal estimation approach, based on the framework of Gaussian directed acyclic graphs, to infer causal relationships between the knocked-out gene and a large set of other genes. In a simulation study, we found that our proposed method accurately differentiates between downstream causal relationships and those that are upstream or simply associative. It also enables an estimation of the total causal effects between the gene of interest and the remaining genes. Our method performed very similarly to a classical differential analysis for experiments with a relatively large number of biological replicates, but has the advantage of providing a formal causal interpretation. Our proposed marginal causal approach is computationally efficient and may be applied to several thousands of genes simultaneously. In addition, it may help highlight subsets of genes of interest for a more thorough subsequent causal network inference. The method is implemented in an R package called MarginalCausality (available on GitHub). PMID:28301504

  14. Identification of marginal causal relationships in gene networks from observational and interventional expression data.

    PubMed

    Monneret, Gilles; Jaffrézic, Florence; Rau, Andrea; Zerjal, Tatiana; Nuel, Grégory

    2017-01-01

    Causal network inference is an important methodological challenge in biology as well as other areas of application. Although several causal network inference methods have been proposed in recent years, they are typically applicable for only a small number of genes, due to the large number of parameters to be estimated and the limited number of biological replicates available. In this work, we consider the specific case of transcriptomic studies made up of both observational and interventional data in which a single gene of biological interest is knocked out. We focus on a marginal causal estimation approach, based on the framework of Gaussian directed acyclic graphs, to infer causal relationships between the knocked-out gene and a large set of other genes. In a simulation study, we found that our proposed method accurately differentiates between downstream causal relationships and those that are upstream or simply associative. It also enables an estimation of the total causal effects between the gene of interest and the remaining genes. Our method performed very similarly to a classical differential analysis for experiments with a relatively large number of biological replicates, but has the advantage of providing a formal causal interpretation. Our proposed marginal causal approach is computationally efficient and may be applied to several thousands of genes simultaneously. In addition, it may help highlight subsets of genes of interest for a more thorough subsequent causal network inference. The method is implemented in an R package called MarginalCausality (available on GitHub).

  15. Causal capture effects in chimpanzees (Pan troglodytes).

    PubMed

    Matsuno, Toyomi; Tomonaga, Masaki

    2017-01-01

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

  16. Norms and customs: causally important or causally impotent?

    PubMed

    Jones, Todd

    2010-01-01

    In this article, I argue that norms and customs, despite frequently being described as being causes of behavior in the social sciences and ordinary conversation, cannot really cause behavior. Terms like "norms" and the like seem to refer to philosophically disreputable disjunctive properties. More problematically, even if they do not, or even if there can be disjunctive properties after all, I argue that norms and customs still cannot cause behavior. The social sciences would be better off without referring to properties like norms and customs as if they could be causal.

  17. Reducing children's behavior problems through social capital: A causal assessment.

    PubMed

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

    2017-01-01

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

  18. CADDIS Volume 1. Stressor Identification: About Causal Assessment

    EPA Pesticide Factsheets

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

  19. 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.

  20. Distinguishing causal interactions in neural populations.

    PubMed

    Seth, Anil K; Edelman, Gerald M

    2007-04-01

    We describe a theoretical network analysis that can distinguish statistically causal interactions in population neural activity leading to a specific output. We introduce the concept of a causal core to refer to the set of neuronal interactions that are causally significant for the output, as assessed by Granger causality. Because our approach requires extensive knowledge of neuronal connectivity and dynamics, an illustrative example is provided by analysis of Darwin X, a brain-based device that allows precise recording of the activity of neuronal units during behavior. In Darwin X, a simulated neuronal model of the hippocampus and surrounding cortical areas supports learning of a spatial navigation task in a real environment. Analysis of Darwin X reveals that large repertoires of neuronal interactions contain comparatively small causal cores and that these causal cores become smaller during learning, a finding that may reflect the selection of specific causal pathways from diverse neuronal repertoires.

  1. An introduction to causal modeling in clinical trials.

    PubMed

    Bellamy, Scarlett L; Lin, Julia Y; Ten Have, Thomas R

    2007-01-01

    We review and compare two causal modeling approaches that correspond to two major and distinct classes of inference - efficacy and intervention-based inference - in the context of randomized trials with subject noncompliance. We review the definitions of efficacy and intervention-based effects in the clinical trials literature and relate these to two separate and distinct causal modeling approaches: the structural mean modeling (SMM) approach and the principal stratification, instrumental variable approach. The SMM-based efficacy approach focuses on the effect of actually receiving treatment. In contrast, the principal stratification method addresses the effect of treatment assignment within partially unobserved latent subgroups defined by compliance behavior. While these approaches differ in terms of philosophy, model definitions, and estimation, they estimate the same causal effect under certain assumptions, but estimate very different causal effects when those assumptions are relaxed. We illustrate these results using a randomized psychiatry trial where the focus is physician compliance to the designated protocol and the other examines patient compliance to the designated protocol, both from the same trial. The validity of the models under the instrumental variable, SMM and principal stratification approaches depends on modeling assumptions, some of which may not be verifiable from the observed data and potentially less realistic than the no-confounding assumption made by non-causal approaches. This comparison in terms of efficacy versus intervention-based effects in causal modeling parallels the explanatory versus pragmatic approaches in clinical trials research; therefore researchers should weigh carefully when choosing causal modeling methodology based on whether efficacy or intervention-based effects are of interest.

  2. Causal Asymmetry Across Cultures: Assigning Causal Roles in Symmetric Physical Settings

    PubMed Central

    Bender, Andrea; Beller, Sieghard

    2011-01-01

    Causal cognition in the physical domain has been treated for a long time as if it were (1) objective and (2) independent of culture. Despite some evidence to the contrary, however, these implicit assumptions have been rarely ever explored systematically. While the pervasive tendency of people to consider one of two equally important entities as more important for bringing about an effect (as reported by White, 2006) meanwhile provides one type of counter-evidence for the first assumption, respective findings remained mute to the second. In order to scrutinize how robust such tendencies are across cultures – and, if not, on which aspects of culture they may depend – we asked German and Tongan participants to assign prime causality in nine symmetric settings. For most settings, strong asymmetries in both cultures were found, but not always in the same direction, depending on the task content and by virtue of the multifaceted character of “culture.” This indicates that causal asymmetries, while indeed being a robust phenomenon across cultures, are also modulated by task-specific properties (such as figure–ground relations), and are subject to cultural influences. PMID:21960982

  3. Tachyon kinematics and causality: a systematic thorough analysis of the tachyon causal paradoxes

    SciTech Connect

    Recami, E.

    1987-03-01

    The chronological order of the events along a spacelike path is not invariant under Lorentz transformations, as is well known. This led to an early conviction that tachyons would give rise to causal anomalies. A relativistic version of the Stueckelberg-Feynman switching procedure (SWP) has been invoked as the suitable tool to eliminate those anomalies. The application of the SWP does eliminate the motions backwards in time, but interchanges the roles of source and detector. This fact triggered the proposal of a host of causal paradoxes. Till now, however, it has not been recognized that such paradoxes can be sensibly discussed (and completely solved, at least in microphysics) only after the tachyon relativistic mechanics has been properly developed. They start by showing how to apply the SWP, both in the case of ordinary special relativity and in the case with tachyons. Then they carefully exploit the kinetics of the tachyon exchange between two (ordinary) bodies. Being finally able to tackle the tachyon causality problem, they successively solve the paradoxes of: (i) Tolman-Regge, (ii) Pirani, (iii) Edmonds, and (iv) Bell. Finally, they discuss a further, new paradox associated with the transmission of signals by modulated tachyon beams.

  4. Glucose-induced cytosolic pH changes in beta-cells and insulin secretion are not causally related: studies in islets lacking the Na+/H+ exchangeR NHE1.

    PubMed

    Stiernet, Patrick; Nenquin, Myriam; Moulin, Pierre; Jonas, Jean-Christophe; Henquin, Jean-Claude

    2007-08-24

    The contribution of Na(+)/H(+) exchange (achieved by NHE proteins) to the regulation of beta-cell cytosolic pH(c), and the role of pH(c) changes in glucose-induced insulin secretion are disputed and were examined here. Using real-time PCR, we identified plasmalemmal NHE1 and intracellular NHE7 as the two most abundant NHE isoforms in mouse islets. We, therefore, compared insulin secretion, cytosolic free Ca(2+) ([Ca(2+)](c)) and pH(c) in islets from normal mice and mice bearing an inactivating mutation of NHE1 (Slc9A1-swe/swe). The experiments were performed in HCO(-)(3)/CO(2) or HEPES/NaOH buffers. PCR and functional approaches showed that NHE1 mutant islets do not express compensatory pH-regulating mechanisms. NHE1 played a greater role than HCO(-)(3)-dependent mechanisms in the correction of an acidification imposed by a pulse of NH(4)Cl. In contrast, basal pH(c) (in low glucose) and the alkalinization produced by high glucose were independent of NHE1. Dimethylamiloride, a classic blocker of Na(+)/H(+) exchange, did not affect pH(c) but increased insulin secretion in NHE1 mutant islets, indicating unspecific effects. In control islets, glucose similarly increased [Ca(2+)](c) and insulin secretion in HCO(-)(3) and HEPES buffer, although pH(c) changed in opposite directions. The amplification of insulin secretion that glucose produces when [Ca(2+)](c) is clamped at an elevated level by KCl was also unrelated to pH(c) and pH(c) changes. All effects of glucose on [Ca(2+)](c) and insulin secretion proved independent of NHE1. In conclusion, NHE1 protects beta-cells against strong acidification, but has no role in stimulus-secretion coupling. The changes in pH(c) produced by glucose involve HCO(-)(3)-dependent mechanisms. Variations in beta-cell pH(c) are not causally related to changes in insulin secretion.

  5. 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.

  6. 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.

  7. Immunity in arterial hypertension: associations or causalities?

    PubMed Central

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

    2015-01-01

    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. PMID:25762356

  8. [Antibibiotic resistance by nosocomial infections' causal agents].

    PubMed

    Salazar-Holguín, Héctor Daniel; Cisneros-Robledo, María Elena

    2016-01-01

    The antibibiotic resistance by nosocomial infections (NI) causal agents constitutes a seriously global problematic that involves the Mexican Institute of Social Security's Regional General Hospital 1 in Chihuahua, Mexico; although with special features that required to be specified and evaluated, in order to concrete an effective therapy. Observational, descriptive and prospective study; by means of active vigilance all along 2014 in order to detect the nosocomial infections, for epidemiologic study, culture and antibiogram to identify its causal agents and antibiotics resistance and sensitivity. Among 13527 hospital discharges, 1079 displayed NI (8 %), standed out: the related on vascular lines, of surgical site, pneumonia and urinal track; they added up two thirds of the total. We carried out culture and antibiogram about 300 of them (27.8 %); identifying 31 bacterian species, mainly seven of those (77.9 %): Escherichia coli, Staphylococcus aureus and epidermidis, Pseudomonas aeruginosa, Acinetobacter baumannii, Klebsiella pneumoniae and Enterobacter cloacae; showing multiresistance to 34 tested antibiotics, except in seven with low or without resistance at all: vancomycin, teicoplanin, linezolid, quinupristin-dalfopristin, piperacilin-tazobactam, amikacin and carbapenems. When we contrasted those results with the recommendations in the clinical practice guides, it aroused several contradictions; so they must be taken with reserves and has to be tested in each hospital, by means of cultures and antibiograms in practically every case of nosocomial infection.

  9. Recursive partitioning for heterogeneous causal effects.

    PubMed

    Athey, Susan; Imbens, Guido

    2016-07-05

    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%.

  10. 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.

  11. Information flow and causality as rigorous notions ab initio.

    PubMed

    Liang, X San

    2016-11-01

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

  12. Information flow and causality as rigorous notions ab initio

    NASA Astrophysics Data System (ADS)

    Liang, X. San

    2016-11-01

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

  13. Formalizing Neurath's ship: Approximate algorithms for online causal learning.

    PubMed

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

    2017-04-01

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

  14. Causal impulse response for circular sources in viscous media

    PubMed Central

    Kelly, James F.; McGough, Robert J.

    2008-01-01

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

  15. Analyzing brain networks with PCA and conditional Granger causality.

    PubMed

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

    2009-07-01

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

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

    PubMed

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

    2013-12-03

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

  17. Quantifying causal emergence shows that macro can beat micro

    PubMed Central

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

    2013-01-01

    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. PMID:24248356

  18. Methodologic implications of the Precautionary Principle: causal criteria.

    PubMed

    Weed, Douglas L

    2004-01-01

    Applying the Precautionary Principle to public health requires a re-evaluation of the methods of inference currently used to make claims about disease causation from epidemiologic and other forms of scientific evidence. In current thinking, a well-established, near-certain causal relationship implies highly consistent statistically significant results across many different studies, large relative risk estimates, extensive understanding of biological mechanisms and dose-response relationships, positive prevention trial results, a clear temporal relationship between cause and effect, and other conditions spelled out in terms of the widely-used causal criteria. The Precautionary Principle, however, states that preventive measures are to be taken when cause and effect relationships are not fully established scientifically. What evidentiary conditions, as reflected in the causal criteria, will be certain enough to warrant precautionary preventive action? This paper argues that minimum evidentiary requirements for causation need to be articulated if the Precautionary Principle is to be successfully incorporated into public health practice. Two precautionary changes to criteria-based methods of causal inference are examined: reducing the number of criteria and weakening the rules of inference accompanying the criteria. Such changes point in the direction of identifying minimum evidentiary conditions, but would be premature without better understanding how well current methods of causal inference work.

  19. 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. © 2013.

  20. 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.

  1. Information-theoretic implications of quantum causal structures.

    PubMed

    Chaves, Rafael; Majenz, Christian; Gross, David

    2015-01-06

    It is a relatively new insight of classical statistics that empirical data can contain information about causation rather than mere correlation. First algorithms have been proposed that are capable of testing whether a presumed causal relationship is compatible with an observed distribution. However, no systematic method is known for treating such problems in a way that generalizes to quantum systems. Here, we describe a general algorithm for computing information-theoretic constraints on the correlations that can arise from a given causal structure, where we allow for quantum systems as well as classical random variables. The general technique is applied to two relevant cases: first, we show that the principle of information causality appears naturally in our framework and go on to generalize and strengthen it. Second, we derive bounds on the correlations that can occur in a networked architecture, where a set of few-body quantum systems is distributed among some parties.

  2. 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.

  3. Causality, menopause, and depression: a critical review of the literature.

    PubMed Central

    Nicol-Smith, L.

    1996-01-01

    OBJECTIVE: To assess whether causal criteria can be used to find out whether there is support in published research for maintaining that menopause causes depression. DESIGN: Ninety four articles from 30 years of research examining the relation of natural menopause to depression were traced by using Medline and systematic follow up of reference lists. Specified exclusion and inclusion criteria were applied, and the resulting 43 epidemiological primary research articles were classified and tabulated according to sample and measures used and the researchers' own conclusion as to whether or not an association had been established. This material was qualitatively evaluated with Hill's nine criteria for causality. RESULT: There is insufficient evidence at present to maintain that menopause causes depression. In addition to methodological and statistical problems, a temporal problem in the menopause concept hinders research in this area. CONCLUSION: Causal criteria can usefully be used to structure a literature review. Further theoretical work is required to integrate standard clinical epidemiological concepts. PMID:8939110

  4. Feature inference and the causal structure of categories.

    PubMed

    Rehder, Bob; Burnett, Russell C

    2005-05-01

    The purpose of this article was to establish how theoretical category knowledge-specifically, knowledge of the causal relations that link the features of categories-supports the ability to infer the presence of unobserved features. Our experiments were designed to test proposals that causal knowledge is represented psychologically as Bayesian networks. In five experiments we found that Bayes' nets generally predicted participants' feature inferences quite well. However, we also observed a pervasive violation of one of the defining principles of Bayes' nets-the causal Markov condition-because the presence of characteristic features invariably led participants to infer yet another characteristic feature. We argue that this effect arises from a domain-general bias to assume the presence of underlying mechanisms associated with the category. Specifically, people take an exemplar to be a "well functioning" category member when it has most or all of the category's characteristic features, and thus are likely to infer a characteristic value on an unobserved dimension.

  5. Comparison theorems for causal diamonds

    NASA Astrophysics Data System (ADS)

    Berthiere, Clément; Gibbons, Gary; Solodukhin, Sergey N.

    2015-09-01

    We formulate certain inequalities for the geometric quantities characterizing causal diamonds in curved and Minkowski spacetimes. These inequalities involve the redshift factor which, as we show explicitly in the spherically symmetric case, is monotonic in the radial direction, and it takes its maximal value at the center. As a by-product of our discussion we rederive Bishop's inequality without assuming the positivity of the spatial Ricci tensor. We then generalize our considerations to arbitrary, static and not necessarily spherically symmetric, asymptotically flat spacetimes. In the case of spacetimes with a horizon our generalization involves the so-called domain of dependence. The respective volume, expressed in terms of the duration measured by a distant observer compared with the volume of the domain in Minkowski spacetime, exhibits behaviors which differ if d =4 or d >4 . This peculiarity of four dimensions is due to the logarithmic subleading term in the asymptotic expansion of the metric near infinity. In terms of the invariant duration measured by a comoving observer associated with the diamond we establish an inequality which is universal for all d . We suggest some possible applications of our results including comparison theorems for entanglement entropy, causal set theory, and fundamental limits on computation.

  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. Achievement in Mother Tongue Literature: Some Strategies of Causal Analysis.

    ERIC Educational Resources Information Center

    Bulcock, Jeffrey W.

    Three stages of linear causal model building procedures (conceptual, main theory, and auxiliary theory) were used to examine the cultural and personality resources of individuals and their school-related skills as determinants of achievement in mother tongue literature. A path analytic approach was used to test a popular model of literature…

  9. The Role of Causal Models in Analogical Inference

    ERIC Educational Resources Information Center

    Lee, Hee Seung; Holyoak, Keith J.

    2008-01-01

    Computational models of analogy have assumed that the strength of an inductive inference about the target is based directly on similarity of the analogs and in particular on shared higher order relations. In contrast, work in philosophy of science suggests that analogical inference is also guided by causal models of the source and target. In 3…

  10. Race, Sex, Causal Attribution, and Help-Seeking Behavior.

    ERIC Educational Resources Information Center

    Cheatham, Harold E.; And Others

    1987-01-01

    Examined the help-seeking behaviors of college students needing assistance with personal problems. Using attribution theory and the learned helplessness paradigm, found that race and sex differences but not causal attribution (seeing problems as caused by internal or external factors) were related to seeking out assistance. Discusses the recurrent…

  11. 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.

  12. Timing and causality in the generation of learned eyelid responses.

    PubMed

    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.

  13. Causality and the Interpretation of Epidemiologic Evidence

    PubMed Central

    Kundi, Michael

    2006-01-01

    There is an ongoing debate regarding how and when an agent’s or determinant’s impact can be interpreted as causation with respect to some target disease. The so-called criteria of causation, originating from the seminal work of Sir Austin Bradford Hill and Mervyn Susser, are often schematically applied disregarding the fact that they were meant neither as criteria nor as a checklist for attributing to a hazard the potential of disease causation. Furthermore, there is a tendency to misinterpret the lack of evidence for causation as evidence for lack of a causal relation. There are no criteria in the strict sense for the assessment of evidence concerning an agent’s or determinant’s propensity to cause a disease, nor are there criteria to dismiss the notion of causation. Rather, there is a discursive process of conjecture and refutation. In this commentary, I propose a dialogue approach for the assessment of an agent or determinant. Starting from epidemiologic evidence, four issues need to be addressed: temporal relation, association, environmental equivalence, and population equivalence. If there are no valid counterarguments, a factor is attributed the potential of disease causation. More often than not, there will be insufficient evidence from epidemiologic studies. In these cases, other evidence can be used instead that increases or decreases confidence in a factor being causally related to a disease. Even though every verdict of causation is provisional, action must not be postponed until better evidence is available if our present knowledge appears to demand immediate measures for health protection. PMID:16835045

  14. Children's Causal Inferences from Indirect Evidence: Backwards Blocking and Bayesian Reasoning in Preschoolers

    ERIC Educational Resources Information Center

    Sobel, David M.; Tenenbaum, Joshua B.; Gopnik, Alison

    2004-01-01

    Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a…

  15. Basic Language Skills and Young Children's Understanding of Causal Connections during Storytelling

    ERIC Educational Resources Information Center

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

    2011-01-01

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

  16. 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…

  17. Basic Language Skills and Young Children's Understanding of Causal Connections during Storytelling

    ERIC Educational Resources Information Center

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

    2011-01-01

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

  18. The Role of Mechanism and Covariation Information in Causal Belief Updating

    ERIC Educational Resources Information Center

    Perales, Jose C.; Catena, Andres; Maldonado, Antonio; Candido, Antonio

    2007-01-01

    The present study is aimed at identifying how prior causal beliefs and covariation information contribute to belief updating when evidence, either compatible or contradictory with those beliefs, is provided. Participants were presented with a cover story with which it was intended to activate or generate a causal belief. Variables related to the…

  19. Determining Subjectivity in Text: The Case of Backward Causal Connectives in Dutch

    ERIC Educational Resources Information Center

    Pit, Mirna

    2006-01-01

    This article addresses the question of how to systematically determine the degree of subjectivity expressed in a text, more specifically, the degree of subjectivity expressed in causal coherence relations. The main hypothesis is that the distribution of Dutch backward causal connectives (want, omdat, aangezien, and doordat) can be explained by the…

  20. 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…

  1. 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…

  2. Children's Causal Inferences from Indirect Evidence: Backwards Blocking and Bayesian Reasoning in Preschoolers

    ERIC Educational Resources Information Center

    Sobel, David M.; Tenenbaum, Joshua B.; Gopnik, Alison

    2004-01-01

    Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a…

  3. 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

  4. 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

  5. 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. Copyright © 2013 Elsevier B.V. All rights reserved.

  6. 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.

  7. 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. Copyright © 2010 Elsevier Ltd. All rights reserved.

  8. Mediation and causality at the individual level.

    PubMed

    Bergman, Lars R

    2009-09-01

    Within a person-oriented research paradigm the focus is on individuals characterized by patterns of information that are regarded as indivisible wholes. It is then not sufficient to carry out standard variable-oriented mediation analysis. The procedure suggested by von Eye, Mun, and Mair (2009) for pattern-oriented mediation analysis is much better aligned to this person-oriented framework. An important new feature in their approach is that it can detect mediator configurations that prohibit predictor and outcome connections at a pattern level. Two extensions of their procedure are suggested, namely (1) the use of cluster analysis to arrive at the categories and (2) the use of other models for estimating the expected frequencies. It is pointed out that in their context a functional relations perspective might be more relevant than the standard causality perspective.

  9. Causal modeling of panic disorder theories.

    PubMed

    Fava, Leonardo; Morton, John

    2009-11-01

    We compare a variety of theories of panic disorder using a neutral framework: causal modeling. The framework requires identification of key constructs and specification of their interaction. Biological, cognitive, and behavioral elements of the theory have to be clearly distinguished, as do critical past events and current trigger conditions. The theories compared were drawn from the psycho-dynamic, cognitive, and neurobiological literature. We conclude that there are substantive differences among the cognitive theories and between the biological theories reviewed. However, cognitive and biological theories appear to be compatible in principle. It is not clear whether substantive differences among theories are due to the existence of subtypes of PD or due to the predominance of multifactorial cause. It is argued that current treatment methods imply particular theories, and that particular patterns of success and failure can be understood in relation to theory through the methods we have employed.

  10. Population heterogeneity and causal inference.

    PubMed

    Xie, Yu

    2013-04-16

    Population heterogeneity is ubiquitous in social science. The very objective of social science research is not to discover abstract and universal laws but to understand population heterogeneity. Due to population heterogeneity, causal inference with observational data in social science is impossible without strong assumptions. Researchers have long been concerned with two potential sources of bias. The first is bias in unobserved pretreatment factors affecting the outcome even in the absence of treatment. The second is bias due to heterogeneity in treatment effects. In this article, I show how "composition bias" due to population heterogeneity evolves over time when treatment propensity is systematically associated with heterogeneous treatment effects. A form of selection bias, composition bias, arises dynamically at the aggregate level even when the classic assumption of ignorability holds true at the microlevel.

  11. Population heterogeneity and causal inference

    PubMed Central

    Xie, Yu

    2013-01-01

    Population heterogeneity is ubiquitous in social science. The very objective of social science research is not to discover abstract and universal laws but to understand population heterogeneity. Due to population heterogeneity, causal inference with observational data in social science is impossible without strong assumptions. Researchers have long been concerned with two potential sources of bias. The first is bias in unobserved pretreatment factors affecting the outcome even in the absence of treatment. The second is bias due to heterogeneity in treatment effects. In this article, I show how “composition bias” due to population heterogeneity evolves over time when treatment propensity is systematically associated with heterogeneous treatment effects. A form of selection bias, composition bias, arises dynamically at the aggregate level even when the classic assumption of ignorability holds true at the microlevel. PMID:23530202

  12. 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.

  13. 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.

  14. A hierarchical causal taxonomy of psychopathology across the life span.

    PubMed

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

    2017-02-01

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

  15. 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

  16. Causal models and learning from data: integrating causal modeling and statistical estimation.

    PubMed

    Petersen, Maya L; van der Laan, Mark J

    2014-05-01

    The practice of epidemiology requires asking causal questions. Formal frameworks for causal inference developed over the past decades have the potential to improve the rigor of this process. However, the appropriate role for formal causal thinking in applied epidemiology remains a matter of debate. We argue that a formal causal framework can help in designing a statistical analysis that comes as close as possible to answering the motivating causal question, while making clear what assumptions are required to endow the resulting estimates with a causal interpretation. A systematic approach for the integration of causal modeling with statistical estimation is presented. We highlight some common points of confusion that occur when causal modeling techniques are applied in practice and provide a broad overview on the types of questions that a causal framework can help to address. Our aims are to argue for the utility of formal causal thinking, to clarify what causal models can and cannot do, and to provide an accessible introduction to the flexible and powerful tools provided by causal models.

  17. 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…

  18. 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…

  19. 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

  20. Causal Anomalies in Kaluza-Klein Gravity Theories

    NASA Astrophysics Data System (ADS)

    Rebouças, M. J.; Teixeira, A. F. F.

    Causal anomalies in two Kaluza-Klein gravity theories are examined, particularly as to whether these theories permit solutions in which the causality principle is violated. It is found that similarly to general relativity the field equations of the space-time-mass Kaluza-Klein (STM-KK) gravity theory do not exclude violation of causality of Gödel type, whereas the induced matter Kaluza-Klein (IM-KK) gravity rules out noncausal Gödel-type models. The induced matter version of general relativity is shown to be an efficient therapy for causal anomalies that occurs in a wide class of noncausal geometries. Perfect fluid and dust Gödel-type solutions of the STM-KK field equations are studied. It is shown that every Gödel-type perfect fluid solution is isometric to the unique dust solution of the STM-KK field equations. The question as to whether 5D Gödel-type noncausal geometries induce any physically acceptable 4D energy-momentum tensor is also addressed.