Previous work suggests that humans find it difficult to learn the structure of causal systems given observational data alone. We show that structure learning is successful when the causal sys- tems in question are consistent with people’s expectations that causal relationships are deterministic and that each pattern of observations has a single underlying cause. Our data are well explained by a Bayesian model that incorporates a preference for symmetric structures and a preference for structures that make the observed data not only possible but likely.