Open Access
August 2010 To Explain or to Predict?
Galit Shmueli
Statist. Sci. 25(3): 289-310 (August 2010). DOI: 10.1214/10-STS330


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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Galit Shmueli. "To Explain or to Predict?." Statist. Sci. 25 (3) 289 - 310, August 2010.


Published: August 2010
First available in Project Euclid: 4 January 2011

zbMATH: 1329.62045
MathSciNet: MR2791669
Digital Object Identifier: 10.1214/10-STS330

Keywords: causality , data mining , Explanatory modeling , predictive modeling , predictive power , scientific research , statistical strategy

Rights: Copyright © 2010 Institute of Mathematical Statistics

Vol.25 • No. 3 • August 2010
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