Causal Inference and Causal Explanation

Abstract

Wesley Salmon’s account of causal inference and causal explanation is, very briefly, as follows: causality is a feature of processes, a feature they have in virtue of being spatio-temporally connected and of bearing a mark or marks (that is, a property the process acquires in virtue of an interaction with another process) from one space-time region to another, later one, without benefit of subsequent interaction. (“Interaction” is not supposed to be a causal primitive: two processes interact if they occupy a common space-time region — that is, if they intersect — and satisfy a statistical condition.1) The explanation of particular occurrences is always causal explanation, but Salmon allows that the concern of science is seldom to explain particular occurrences, and more often is to explain regularities. I am unsure what form Salmon supposes causal explanations, whether of particular occurrences or of patterns, to have, and I am likewise unsure how the emphasis in his more recent essays on causal explanation and the irreducibility of causal relations to statistical relations combines with his earlier emphasis on statistical explanation as a kind of sui generis form. I will simply construe his views on these matters in what I take to be both the most clear-cut and the most generous way: there is no such thing as statistical explanation per se, merely statistical evidence for (and, perhaps, statistical aspects of) causal explanations. The causal explanation of a particular event consists in the description of any interesting fragment of the causal history leading up to the event.