 A causal model makes predictions about the behavior of a system. In particular, a causal model entails the truth value, or the probability, of counterfactual claims about the system; it predicts the effects of interventions; and it entails the probabilistic dependence or independence of variables included in the model. Causal models also facilitate the inverse of these inferences: if we have observed probabilistic correlations among variables, or the outcomes of experimental interventions, we can determine which causal models are consistent with these observations. The discussion will focus on what it is possible to do in “in principle”. For example, we will consider the extent to which we can infer the correct causal structure of a system, given perfect information about the probability distribution over the variables in the system. This ignores the very real problem of inferring the true probabilities from finite sample data. In addition, the entry will discuss the application of causal models to the logic of counterfactuals, the analysis of causation, and decision theory.