Contrastive, non-probabilistic statistical explanations

Philosophy of Science 65 (3):448-471 (1998)
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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.

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Bruce Glymour
Kansas State University

References found in this work

From covariation to causation: A causal power theory.Patricia W. Cheng - 1997 - Psychological Review 104 (2):367-405.
Statistical explanation.Wesley C. Salmon - 1970 - In Robert Colodny (ed.), The Nature and Function of Scientific Theories. University of Pittsburgh Press. pp. 173--231.
The Logic of Reliable Inquiry.Kevin Kelly - 1998 - British Journal for the Philosophy of Science 49 (2):351-354.
Causality without counterfactuals.Wesley C. Salmon - 1994 - Philosophy of Science 61 (2):297-312.

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