A generalized probabilistic theory of causal relevance

I advance a new theory of causal relevance, according to which causal claims convey information about conditional probability functions. This theory is motivated by the problem of disjunctive factors, which haunts existing probabilistic theories of causation. After some introductory remarks, I present in Section 3 a sketch of Eells's (1991) probabilistic theory of causation, which provides the framework for much of the discussion. Section 4 explains how the problem of disjunctive factors arises within this framework. After rejecting three proposed solutions, I offer in Section 6 a new approach to causation that avoids the problem. Decision-theoretic considerations also support the new approach. Section 8 develops the consequences of the new theory for causal explanation. The resulting theory of causal explanation incorporates the new insights while respecting important work on scientific explanation by Salmon (1971), Railton (1981), and Humphreys (1989). My conclusions are enumerated in Section 9.I would like to thank Nuel Belnap, John Earman, Richard Gale, Paul Humphreys, Satish Iyengar, Wes Salmon, and two anonymous referees for comments and discussion. I am also indebted to the members of an audience at the Center for Philosophy of Science at the University of Pittsburgh, where some of the ideas contained in this paper were presented.