 Besides clarifying its general interpretation of probability and utility, causal decision theory searches for the particular probabilities and utilities that yield the best version of its principle to maximize expected utility. The causal probabilities in its formula for expected utility may be probabilities of subjunctive conditionals or various substitutes. Versions that use probabilities of subjunctive conditionals must settle on an analysis of those conditionals. Lewis (1973: Chap. 1) modifies Stalnaker’s analysis to count a subjunctive conditional true if and only if as antecedent worlds come closer and closer to the actual world, there is a point beyond which the consequent is true in all the worlds at least that close. Joyce (1999: 161–180) advances probability images, as Lewis (1976) introduces them, as substitutes for probabilities of subjunctive conditionals. The probability image of a state \(S\) under subjunctive supposition of an act \(A\) is the probability of \(S\) according to an assignment that shifts the probability of \({\sim}A\)-worlds to nearby \(A\)-worlds. Causal relations among an act and possible states guide probability’s reassignment.