A Probabilistic Analysis of Causation

The starting point in the development of probabilistic analyses of token causation has usually been the naIve intuition that, in some relevant sense, a cause raises the probability of its effect. But there are well-known examples both of non-probability-raising causation and of probability-raising non-causation. Sophisticated extant probabilistic analyses treat many such cases correctly, but only a t the cost of excluding the possibilities of direct non-probability-raising causation, failures of causal transitivity, action-ata-distance, prevention, and causation by absence and omission. I show that an examination of the structure of these problem cases suggests a different treatment, one which avoids the costs of extant probabilistic analyses.