 Judea Pearl (2000) and also Peter Spirtes, Clark Glymour, and Richard Scheines (2000) present methods of inferring causal relations from statistical data. They use directed acyclic graphs and associated probability distributions to construct causal models. In a decision problem, a causal model yields a way of calculating an act’s effect. A causal graph and its probability distribution express a dependency hypothesis and yield each act’s causal influence given that hypothesis. They specify the causal probability of a state under supposition of an act. An act’s expected utility is a probability-weighted average of its expected utility according to the dependency hypotheses that candidate causal models represent, as Weirich (2015: 225–236) explains.