Models of covariation-based causal judgment: a review and synthesis

Causal judgment is assumed to play a central role in prediction, control, and explanation. Here, we consider the function or functions that map contingency information concerning the relationship between a single cue and a single outcome onto causal judgments. We evaluate normative accounts of causal induction and report the findings of an extensive meta-analysis in which we used a cross-validation model-fitting method and carried out a qualitative analysis of experimental trends in order to compare a number of alternative models. The best model to emerge from this competition is one in which judgments are based on the difference between the amount of confirming and disconfirming evidence. A rational justification for the use of this model is proposed.