The role of causal models in multiple judgments under uncertainty

• We derive and test predictions of a causal Bayes net account of judgment under uncertainty across multiple observations.
• Causal explanation of false positives promoted stability in probability estimates across multiple observations.
• Statistics without an apparent cause were treated as stochastic in intuitive probability judgments.
• Identical observed events can lead to different probability judgments depending on causal beliefs about the events.

Two studies examined a novel prediction of the causal Bayes net approach to judgments under uncertainty, namely that causal knowledge affects the interpretation of statistical evidence obtained over multiple observations. Participants estimated the conditional probability of an uncertain event (breast cancer) given information about the base rate, hit rate (probability of a positive mammogram given cancer) and false positive rate (probability of a positive mammogram in the absence of cancer). Conditional probability estimates were made after observing one or two positive mammograms. Participants exhibited a causal stability effect: there was a smaller increase in estimates of the probability of cancer over multiple positive mammograms when a causal explanation of false positives was provided. This was the case when the judgments were made by different participants (Experiment 1) or by the same participants (Experiment 2). These results show that identical patterns of observed events can lead to different estimates of event probability depending on beliefs about the generative causes of the observations.