Structured correlation from the causal background

Previous research has cast doubt on whether the Markov con-dition is a default assumption of human causal reasoning—as causal Bayes net approaches suggest. Human subjects often seem to violate the Markov condition in common-cause rea-soning tasks. While this might be treated as evidence that humans are inefficient causal reasoners, we propose that the underlying human intuitions reflect abstract causal knowledge that is sensitive to a great deal of contextual information— knowledge of the “causal background”. In this paper, we in-troduce a hierarchical Bayesian model of causal background knowledge which explains Markov violations and makes addi-tional, more fine-grained predictions on the basis of causally relevant category membership. We confirm these predictions using an experimental paradigm which extends that used in previous studies of “Markov violation.