On the Explanatory Depth and Pragmatic Value of Coarse-Grained, Probabilistic, Causal Explanations

This article considers the thesis that a more proportional relationship between a cause and its effect yields a more abstract causal explanation of that effect, thereby producing a deeper explanation. This thesis has important implications for choosing the optimal granularity of explanation for a given explanandum. In this article, I argue that this thesis is not generally true of probabilistic causal relationships. In light of this finding, I propose a pragmatic measure of explanatory depth. This measure uses a decision-theoretic model of information pricing to determine the optimal granularity of explanation for a given explanandum, agent, and decision problem.