Abstract

Salmon has argued that science provides explanations by describing a causal nexus: For Salmon, this nexus is a network of processes and interactions. I argue that this picture of the causal nexus is insufficient for an account of scientific explanation: a taxonomy of causal relevance is also needed. ;Probabilistic theories of causation seem to provide such a taxonomy in their dichotomy between promoting and inhibiting causes. However, standard probabilistic theories are beset by a difficulty called the problem of disjunctive factors. According to such theories, an effect is more probable in the presence of a cause than in its absence . But there are many ways in which a particular cause may come about, and many in which it may be absent; these ways need not confer the same probabilities upon the effect. Thus, there is no direct comparison of two probabilities to be made. My solution is to abandon the oversimplified dichotomy between promoting and inhibiting causes. Causal claims convey qualitative information about more complex probabilistic relationships. Interestingly, this picture of causation meshes naturally with extant theories of explanation. ;I show how this framework may be employed in resolving several outstanding problems in the theories of causation and explanation, such as the relationship between singular and general causal claims, and the role of contrastive stress in causal and explanatory claims. ;Finally, I show how my account of causal explanation can be connected with the semantic conception of scientific theories. According to this conception, a theory is characterized in terms of a class of mathematical models. I describe the relationship between these mathematical models and the probabilistic relationships that are described by causal explanations. These ideas are illustrated in an extended discussion of causation and explanation in evolutionary theory