Causal inference in the empiricalsciences is based on counterfactuals. The mostcommon approach utilizes a statistical model ofpotential outcomes to estimate causal effectsof treatments. On the other hand, one leadingapproach to the study of causation inphilosophical logic has been the analysis ofcausation in terms of counterfactualconditionals. This paper discusses and connectsboth approaches to counterfactual causationfrom philosophy and statistics. Specifically, Ipresent the counterfactual account of causationin terms of Lewis's possible-world semantics,and reformulate the statistical potentialoutcome framework using counterfactualconditionals. This procedure highlights variousproperties and (...) mechanisms of the statisticalmodel. (shrink)

George Molnar’s contention that some dispositional properties are displayed without the aid of any activating conditions poses a challenge to the conditional analysis of dispositions. Since the invocation of activating conditions is regarded as a crucial feature of the analysis, spontaneous dispositions are believed to expose its inadequacy by eluding its scope. The challenge goes to the very heart of the conditional approach to dispositions, allegedly revealing a deep flaw in all its incarnations. Granting that there may be spontaneously manifestable (...) dispositions, I argue that the conditional analysis can successfully accommodate them by elevating the potential causal relation between a disposition and its manifestation. (shrink)

The paper attempts to reconcile two very different approaches to the concept of causation. In the original form, it is the opposition found in Laplace between his doctrine of constant and variable causes on the one hand and his mechanistic determinism on the other. This tension was described clearly only by Maxwell who stressed the contrast between the statistical and the dynamical method (calling the latter also the historical or strictly kinetic method). A similar dichotomy surfaces in the work of (...) Wesley Salmon who distinguished statistical from aleatory causation. Acknowledging that we seem to have conflicting intuitions about causality and explanation, I argue that assigning probabilistic laws a fundamental role in scientific explanation does not conflict with the assumption of Laplacian determinism. This is due to the fact that in most systems of interest, we confront a complex interaction of multiple causal factors and an extreme sensitivity to small variations of initial conditions. (shrink)