Non-Bayesian Inference: Causal Structure Trumps Correlation

Bénédicte Bes, Steven Sloman, Christopher G. Lucas, Éric Raufaste

Research output: Contribution to journalArticlepeer-review

Abstract

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 probable when it was linked to evidence by a causal chain than when both variables shared a common cause; (b) predictive chains in which evidence is a cause of the hypothesis gave rise to higher judgments than diagnostic chains in which evidence is an effect of the hypothesis; and (c) direct chains gave rise to higher judgments than indirect chains. A Bayesian learning model was applied to our data but failed to explain them. An explanation-based hypothesis stating that statistical information will affect judgments only to the extent that it changes beliefs about causal structure is consistent with the results.
Original languageEnglish
Pages (from-to)1178-1203
Number of pages26
JournalCognitive Science
Volume36
Issue number7
DOIs
Publication statusPublished - 2012

Keywords

  • Probability judgment, Causal explanations, Bayesian model

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