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Contrastive, Non-Probabilistic Statistical Explanations

Published online by Cambridge University Press:  01 April 2022

Bruce Glymour
Bruce Glymour*
Affiliation:
Department of Philosophy, Kansas State University
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Abstract

Standard models of statistical explanation face two intractable difficulties. In his 1984 Salmon argues that because statistical explanations are essentially probabilistic we can make sense of statistical explanation only by rejecting the intuition that scientific explanations are contrastive. Further, frequently the point of a statistical explanation is to identify the etiology of its explanandum, but on standard models probabilistic explanations often fail to do so. This paper offers an alternative conception of statistical explanations on which explanations of the frequency of a property consist in the derivation of that frequency from a statistical specification of the mechanism by which instances of the relevant property are produced. Such explanations are contrastive precisely because they identify the determinate causal etiologies of their explananda.

Information

Type
Research Article
Information
Philosophy of Science , Volume 65 , Issue 3 , September 1998, pp. 448 - 471
Copyright
Copyright © Philosophy of Science Association 1998

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Footnotes

†

Send reprint requests to the author, Department of Philosophy, 204 Kedzie Hall, Kansas State University, Manhattan Kansas, 66506.

‡

Thanks are due to the members of the University of Kansas Philosophy Department, to whom a version of this paper was delivered, to Brian Elliott, Clark Glymour, Philip Kitcher, Jim Lennox, Michael O'Rourke, and three referees, who all made many helpful suggestions, and to Lynn Trifanoff for help with the illustrations.

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