Causal Models can be Used to Predict Base-Rate Neglect

Causal models are used to predict individuals’ probability judgments on the taxicab problem of Tversky and Kahneman (1982). Predictions are based on the hypothesis that judgments take into account only those variables that are judged causally relevant. Two versions of the problem were tested, one with and one without causally-relevant base rates. The results showed that causal models were able to predict judgments reasonably well. However, the data failed to replicate Tversky and Kahneman’s finding of a difference between the two conditions.