A Counterfactual Analysis of Causation
A counterfactual analysis of causation is developed by distinctive notion of chance-raising characterized by probabilistic Σ-dependence, when a causal chain is complete appealing to chance-raising at a time just before the time of the effect, and a requirement that the causal chain is made up of actual events to avoid the standard problems with conditional analyses, due to potential changes in the circumstances when the antecedents are true. Although the development takes the form of a consideration of difficult cases of causation (especially probabilistic cases of pre-emption), the resulting idea has independent motivation and simplicity. It is that causes of a target event are those which (independently of its competitors) both make the mean chance of an effect very much greater than its mean background chance, and actually influence the probability of the effect in this way, at the time at which the effect occurred via a complete causal chain.