Leading accounts of judgment under uncertainty evaluate performance within purely statistical frameworks, holding people to the standards of classical Bayesian (A. Tversky & D. Kahneman, 1974) or frequentist (G. Gigerenzer & U. Hoffrage, 1995) norms. The authors argue that these frameworks have limited ability to explain the success and flexibility of people's real-world judgments and propose an alternative normative framework based on Bayesian inferences over causal models. Deviations from traditional norms of judgment, such as base-rate neglect, may then be explained in terms of a mismatch between the statistics given to people and the causal models they intuitively construct to support probabilistic reasoning. Four experiments show that when a clear mapping can be established from given statistics to the parameters of an intuitive causal model, people are more likely to use the statistics appropriately, and that when the classical and causal Bayesian norms differ in their prescriptions, people's judgments are more consistent with causal Bayesian norms.