Witteveen, Joeri (2019) Regression explanation and statistical autonomy. [Preprint]

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Abstract

The phenomenon of regression toward the mean is notoriously liable to be overlooked or misunderstood; regression fallacies are easy to commit. But even when regression phenomena are duly recognized, it remains perplexing how they can feature in explanations. This article develops a philosophical account of regression explanations as “statistically autonomous” explanations that cannot be deepened by adducing details about causal histories, even if the explananda as such are embedded in the causal structure of the world. That regression explanations have statistical autonomy was first suggested by Ian Hacking and has recently been defended and elaborated by André Ariew, Yasha Rohwer, and Collin Rice. However, I will argue that these analyses fail to capture what regression’s statistical autonomy consists in and how it sets regression explanations apart from other kinds of explanation. The alternative account I develop also shows what is amiss with a recent denial of regression’s statistical autonomy. Marc Lange has argued that facts that can be explained as regression phenomena can in principle also be explained by citing a conjunction of causal histories. The account of regression explanation developed here shows that his argument is based on a misunderstanding of the nature of statistical autonomy.

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Item Type:	Preprint
Creators:	Creators Email ORCID Witteveen, Joeri jw@ind.ku.dk 0000-0003-1291-9156
Keywords:	regression toward the mean; regression explanation; statistical autonomy; statistical explanation; regression fallacy; reversion; heredity; Francis Galton
Subjects:	Specific Sciences > Biology General Issues > Causation General Issues > Explanation General Issues > History of Science Case Studies General Issues > Models and Idealization Specific Sciences > Probability/Statistics
Depositing User:	Dr. Joeri Witteveen
Date Deposited:	04 Sep 2019 17:12
Last Modified:	04 Sep 2019 17:12
Item ID:	16402
Subjects:	Specific Sciences > Biology General Issues > Causation General Issues > Explanation General Issues > History of Science Case Studies General Issues > Models and Idealization Specific Sciences > Probability/Statistics
Date:	2019
URI:	https://philsci-archive.pitt.edu/id/eprint/16402

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