Causal Generalizations and Good Advice

Abstract

In this essay, I focus on the convergence of two problems. The first is our old nemesis, the reference-class problem. The second is the problem of interpreting causal generalizations such as 'smoking causes lung cancer'. This problem has been with us since the time of Hume. Within the framework of the probabilistic theory of causation (which I have been exploring since the early '90s), this problem takes on a very precise form. All parties to the dispute agree that the truth of such a generalization depends upon certain conditional probabilities; they disagree about the configuration of these probabilities that must obtain in order for the claim to be true. The key to solving this second problem rests in the observation that causal generalizations can serve as guides to life: if I know that smoking causes lung cancer, that gives me a good reason not to smoke. Here is where the two problems converge. One way to formulate the reference-class problem is as follows: which objective, conditional probabilities should guide my decisions? I answer that one should be (partly) guided by the conditional probabilities that figure in the probabilistic theory of causation. This, in turn, helps us to solve the second problem. 'Smoking causes lung cancer' describes a configuration of objective, conditional probabilities, such that if the agent were guided by those probabilities, it would be rational for her to refrain from smoking. Like Hume, I conclude that the truth of a causal generalization is partly a subjective matter.