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Probabilistic causal processes Katz, Jonathan Richard

Abstract

Many theorists take causality to be the heart of scientific explanation. If we couple this view with the idea, again common enough, that causes are something like sufficient conditions for their effects, then we have raised a difficulty for any theory of scientific explanation that admits "final" (genuine) statistical explanations, for a statistical explanation must allow that factors relevant to an event to be explained need not determine that event, but only make it probable to some degree (other than zero or one). If we do not wish to give up the possibility of genuine statistical explanations we must either (1) give up this central causal intuition in explanation, or (2) admit that causes act as something other than sufficient conditions — say in a probabilistic manner. This is, of course, little help unless we can clearly explicate the notion of a probabilistic cause, one of the tasks I have attempted to accomplish in this thesis. The general structure of this effort is in three parts. In the first chapter I generate the basic concepts underlying the notion of a probabilistic cause. In this I follow closely the development of this idea as constructed by Wesley Salmon. Within this view scientific explanation consists in the tracing of causal influence via causes as processes, and in providing the probability relations among events which are the product of causal interactions. In the second chapter I further develop the idea of causal processes by comparing this notion with the more traditional analyses of causation, the regularity and counterfactual theories. There I show how the process ontology very naturally overcomes problems in both of these views. I also note the advantages this conception of causes holds over these received views with respect to the problem of determinism. In the third and final chapter I discuss, and attempt to overcome, difficulties that are specific to the notion of a probabilistic causal process.

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