Causal considerations must be relevant for those making decisions. Whether to bring an umbrella or leave it at home depends on the causal consequences of these options. However, most current decision theories do not address causal reasoning. Here, the authors propose a causal model theory of choice based on causal Bayes nets. The critical ideas are (a) that people decide using causal models of the decision situation and (b) that people conceive of their own choice as an intervention. Four corroborating experiments are reported. The first 2 experiments showed that participants chose on the basis of the causal structure underlying a choice scenario rather than the statistical relation among actions and outcomes. Experiments 3 and 4 showed that participants treated choices and interventions similarly. They also suggest that decision makers use causal models to derive inferences about expected outcomes. Boundary conditions on causal decision making and examples of faulty causal inferences in choice (e.g., self-deception) are discussed. (Contains 2 figures, 5 tables, and 5 footnotes.)