Department of Statistics, UCLA

We present a Bayesian model of causal learning that incorporates generic priors on distributions of weights representing potential powers to either produce or prevent an effect. These generic priors favor necessary and sufficient causes. Across three experiments, the model explains the systematic pattern of human judgments observed for questions regarding support for a causal link, for both generative and preventive causes.