Modeling confidence in causal judgments.

Abstract

Counterfactual theories propose that people's capacity for causal judgment depends on their ability to consider alternative possibilities: The lightning strike caused the forest fire because had it not struck, the forest fire would not have ensued. To accommodate a variety of psychological effects on causal judgment, a range of recent accounts have proposed that people probabilistically sample counterfactual alternatives from which they compute a graded measure of causal strength. While such models successfully describe the influence of the statistical normality (i.e., the base rate) of the candidate and alternate causes on causal judgments, we show that they make further untested predictions about how normality influences people's confidence in their causal judgments. In a large (N = 3,020) sample of participants in a causal judgment task, we found that normality indeed influences people's confidence in their causal judgments and that these influences were predicted by a counterfactual sampling model in which people are more confident in a causal relationship when the effect of the cause is less variable among imagined counterfactual possibilities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).