Proceedings of the Annual Meeting of the Cognitive Science Society

This work provides an alternative account for deviations in human causal reasoning from normative predictions based on Causal Bayesian Networks (CBNs). We highlight violations of the Markov condition (Screening Off) and insufficient Explaining Away. Different from other accounts, our model does not assume that people fail to honor normative predictions due to reliance on heuristics, hidden nodes and links or cognitive limitations. Instead, we propose that people are rationally uncertain about the received causal model they are asked to reason with. We fitted the model to published data from two experiments where people were asked to make probability estimates on inferences of interest within a causal model. We find that the model is able to i) reproduce deviations from normative predictions, and ii) predict changes in the magnitude of these deviations across contexts. We conclude that assuming that people, in order to be rational, will always fully believe in the information they receive about a causal model may be too strong an assumption.