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Cited by 2,517

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To Explain or to Predict?

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Shmueli, Galit·2010·Statistical Science
DOI 10.1214/10-sts330
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Abstract

Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description. In many disciplines there is near-exclusive use of statistical modeling for causal explanation and the assumption that models with high explanatory power are inherently of high predictive power. Conflation between explanation and prediction is common, yet the distinction must be understood for progressing scientific knowledge. While this distinction has been recognized in the philosophy of science, the statistical literature lacks a thorough discussion of the many differences that arise in the process of modeling for an explanatory versus a predictive goal. The purpose of this article is to clarify the distinction between explanatory and predictive modeling, to discuss its sources, and to reveal the practical implications of the distinction to each step in the modeling process.

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Cited by

Metadata

Authors
Shmueli, G.
Venue
Statistical Science
Publisher
Institute of Mathematical Statistics
Published
2010-08-01
Volume
25
Issue
3
Pages
289-310
ISSN
  • 0883-4237 (ISSN-L)
  • 2168-8745
DOI
10.1214/10-sts330
OpenAlex
W2951936974
Cited by
2,517
References
15
FWCI
105.28
Type
journal article
Language
en
Open access
bronze

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