In this article I propose that categorization decisions are often made relative to causal models of categories that people possess. According to this causal-model theory of categorization, evidence of an exemplar's membership in a category consists of the likelihood that such an exemplar can be generated by the category's causal model. Bayesian networks are proposed as a representation of these causal models. Causal-model theory was fit to categorization data from a recent study, and yielded better fits than either the prototype model or the exemplar-based context model, by accounting, for example, for the confirmation and violation of causal relationships and the asymmetries inherent in such relationships.

Although many theories of causal cognition are based on causalgraphical models, a key property of such models—the inde-pendence relations stipulated by the Markov condition—isroutinely violated by human reasoners. Two accounts of whypeople violate independence are formalized and subjected toexperimental test. Subjects’ inferences were more consistentwith a dual prototype model in which people favor networkstates in which variables are all present or all absent than aleaky gate model in which information is transmitted throughnetwork nodes when it should normatively be blocked. Thearticle concludes with a call for theories of causal cognitionthat rest on foundations that are faithful to the kinds of causalinferences people actually draw.