Causal Bayes nets as psychological theories of causal reasoning: evidence from psychological research.
Causal Bayes nets have been developed in philosophy, statistics, and computer sciences to provide a formalism to represent causal structures, to induce causal structure from data and to derive predictions (Glymour and Cooper, in Computation, causation, and discovery, ; Spirtes et al., in Causation,...
| Published in: | Synthese Vol. 193; no. 4; pp. 1107 - 1127 |
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| Format: | Article |
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Springer Nature
Apr2016
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=114677300&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 114677300 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Apr2016 vid: 193 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 114677300 10.1007/s11229-015-0734-0 ppf: 1107 ppct: 20 formats: fmt: @attributes: type: P size: 715KB tig: atl: Causal Bayes nets as psychological theories of causal reasoning: evidence from psychological research. aug: au: Hagmayer, York affil: Georg-Elias-Mueller-Institute for Psychology, University of Goettingen, Gosslerstr. 14 37073 Goettingen Germany su: Psychological research Mathematical variables Causal models Reasoning Empirical research sug: subj: Psychological research Mathematical variables Causal models Reasoning Empirical research keyword: Causal Bayes nets Causal learning and reasoning Psychological theories Rational models ab: Causal Bayes nets have been developed in philosophy, statistics, and computer sciences to provide a formalism to represent causal structures, to induce causal structure from data and to derive predictions (Glymour and Cooper, in Computation, causation, and discovery, ; Spirtes et al., in Causation, prediction, and search, ). Causal Bayes nets have been used as psychological theories in at least two ways. They were used as rational, computational models of causal reasoning (e.g., Gopnik et al., in Psychol Rev 111:3-32, ) and they were used as formal models of mental causal models (e.g., Sloman, in Causal models: how we think about the world and its alternatives, ). A crucial assumption made by them is the Markov condition, which informally states that variables are independent of other variables that are not their direct or indirect effects conditional on their immediate causes. Whether people's inferences conform to the causal Markov and the faithfulness condition has recently been investigated empirically. A review of respective research indicates that inferences frequently violate these conditions. This finding challenges some uses of causal Bayes nets in psychology. They entail that causal Bayes nets may not be appropriate to derive predictions for causal model theories of causal reasoning. They also question whether causal Bayes nets as a rational model are empirically descriptive. They do not challenge, however, causal Bayes nets as normative models and their usage as formal models of causal reasoning. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2016. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2016 holdings: @attributes: islocal: N |
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