Causal explanation improves judgment under uncertainty, but rarely in a Bayesian way

Abstract

Three studies reexamined the claim that clarifying the causal origin of key statistics can increase normative performance on Bayesian problems involving judgment under uncertainty. Experiments 1 and 2 found that causal explanation did not increase the rate of normative solutions. However, certain types of causal explanation did lead to a reduction in the magnitude of errors in probability estimation. This effect was most pronounced when problem statistics were expressed in percentage formats. Experiment 3 used process-tracing methods to examine the impact of causal explanation of false positives on solution strategies. Changes in probability estimation following causal explanation were the result of a mixture of individual reasoning strategies, including non-Bayesian mechanisms, such as increased attention to explained statistics and approximations of subcomponents of Bayes' rule. The results show that although causal explanation of statistics can affect the way that a problem is mentally represented, this does not necessarily lead to an increased rate of normative responding.

Are Jurors Intuitive Statisticians? Bayesian Causal Reasoning in Legal Contexts

“…According to this study, errors in observed diagnostic inferences can often be explained by variations in underlying causal models. Hayes et al (2018) suggested that the role of causal models in normative judgments merits further study. The authors were interested in assessing whether representations of causal models facilitate Bayesian probabilistic judgments in terms of normative accuracy as well as reduction in error magnitude.…”