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

Published online by Cambridge University Press:  01 January 2022


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.

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I am highly indebted to Katie Steele and Luc Bovens for their feedback and support throughout the writing of this article. I am also grateful to the following people for comments and conversations about this article and its subject matter: Jonathan Birch, Hugh Desmond, Phil Dowe, Bryan Roberts, Jeremy Strasser, Philippe van Basshuysen, David Watson, and several anonymous reviewers. This work was presented to audiences at London School of Economics, Australian National University, the 2016 conference of the Dutch Research School of Philosophy in Groningen, the 2017 meeting of the Munich-Sydney-Tilburg Philosophy of Science Group in Sydney, and the 2017 meeting of the Society for Metaphysics of Science at Fordham University.


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