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Contingency information is information about the occurrence or nonoccurrence of an effect when a possible cause is present or absent. Under the evidential evaluation model, instances of contingency information are transformed into evidence and causal judgment is based on the proportion of relevant instances evaluated as confirmatory for the candidate cause. In this article, two experiments are reported that were designed to test systematic manipulations of the proportion of confirming instances in relation to other variables: the proportion of instances on which the candidate cause is present, the proportion of instances in which the effect occurs when the cause is present, and the objective contingency. Results showed that both unweighted and weighted versions of the proportion-of-confirmatory-instances rule successfully predicted the main features of the results, with the weighted version proving more successful. Other models, including the power PC theory, failed to predict the results.

Causal judgment from contingency information: A systematic test of thepCI rule