The starting point in the development of probabilistic analyses of token causation has usually been the naïve 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 at the cost of excluding the possibilities of direct non-probability-raising causation, failures of causal transitivity, action-at-a-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. (shrink)

In Kvart (1991a), I discussed the analysis of causal relevance presented in A Theory of Counterfactuals (1986) (and first in 1975). I explained there in what respect the notion captured by the analysis of Kvart (1986) is a mere approximation to the requisite notion of causal relevance. In this paper I present another analysis of causal relevance, devoid of the shortcoming of its predecessor. The present analysis of causal relevance is, again, grounded in a chancelike notion of objective probability. The (...) correlative notion of causal independence is analyzed as holding in case there is a so-called causal impact series, which is defined here. The basic notions used for this analysis are the notion of a differentiator, which upsets an equiprobability case, and that of a blocker, which restores it. (shrink)