Causal decision theory (CDT) is a school of thought within decision theory which states that, when a rational agent is confronted with a set of possible actions, one should select the action which causes the best outcome in expectation. CDT contrasts with evidential decision theory (EDT), which recommends the action which would be indicative of the best outcome if one received the "news" that it had been taken.

Informally, causal decision theory recommends the agent to make the decision with the best expected causal consequences. For example: if eating an apple will cause you to be happy and eating an orange will cause you to be sad then you would be rational to eat the apple.

One complication is the notion of expected causal consequences. Imagine that eating a good apple will cause you to be happy and eating a bad apple will cause you to be sad but you aren't sure if the apple is good or bad. In this case you don't know the causal effects of eating the apple.

Instead, then, you work from the expected causal effects, where these will depend on three things:

In informal terms, causal decision theory advises the agent to make the decision with the best expected causal effects.

In a 1981 article, Allan Gibbard and William Harper explained causal decision theory as maximization of the expected utility ${\displaystyle U}$ of an action ${\displaystyle A}$ "calculated from probabilities of counterfactuals":

where ${\displaystyle D(O_{j})}$ is the desirability of outcome ${\displaystyle O_{j}}$ and ${\displaystyle P(A>O_{j})}$ is the counterfactual probability that, if ${\displaystyle A}$ were done, then ${\displaystyle O_{j}}$ would hold.

David Lewis proved that the probability of a conditional ${\displaystyle P(A>O_{j})}$ does not always equal the conditional probability ${\displaystyle P(O_{j}|A)}$. (see also Lewis's triviality result) If that were the case, causal decision theory would be equivalent to evidential decision theory, which uses conditional probabilities.