 A causal model’s directed graph and probability distribution indicate causal relations among event types. As Pearl (2000: 30) and Sprites et al. (2000: 11) explain, a causal model meets the causal Markov condition if and only if with respect to its probability distribution each event type in its directed graph is independent of all the event type’s nondescendants, given its parents. Given a model meeting the condition, knowledge of all an event’s direct causes makes other information statistically irrelevant to the event’s occurrence, except for information about the event and its effects. Knowledge of an event’s direct causes screens off evidence from indirect causes and independent effects of its causes. Given a typical causal model for Newcomb’s problem, knowledge of the common cause of a decision and a prediction screens off the correlation between the decision and the prediction.