Whereas the traditional normative benchmark for diagnostic reasoning from effects to causes is provided by purely statistical norms, we here approach the task from the perspective of rational causal inference. The core feature of the presented model is the assumption that diagnostic inferences are constrained by hypotheses about the causal texture of the domain. As a consequence, the models predictions systematically deviate from classical, purely statistical norms of diagnostic inference. In particular, the analysis reveals that diagnostic judgments should not only be influenced by the probability of the cause given the effect, but also be systematically affected by the predictive relation between cause and effect. This prediction is tested in three studies. The obtained pattern of diagnostic reasoning is at variance with the traditional statistical norm but consistent with a model of rational causal inference.