Cognitive Science 34 (2):175-221 (2010)

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Abstract
Bayes nets are formal representations of causal systems that many psychologists have claimed as plausible mental representations. One purported advantage of Bayes nets is that they may provide a theory of counterfactual conditionals, such as If Calvin had been at the party, Miriam would have left early. This article compares two proposed Bayes net theories as models of people's understanding of counterfactuals. Experiments 1-3 show that neither theory makes correct predictions about backtracking counterfactuals (in which the event of the if-clause occurs after the event of the then-clause), and Experiment 4 shows the same is true of forward counterfactuals. An amended version of one of the approaches, however, can provide a more accurate account of these data
Keywords Causal reasoning  Conditionals  Counterfactuals  Bayes nets
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DOI 10.1111/j.1551-6709.2009.01080.x
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References found in this work BETA

Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Cambridge University Press.
Causation.David Lewis - 1973 - Journal of Philosophy 70 (17):556-567.
Causality.Judea Pearl - 2000 - Cambridge University Press.

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Citations of this work BETA

Causal Premise Semantics.Stefan Kaufmann - 2013 - Cognitive Science 37 (6):1136-1170.

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