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to predict to explain

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  a   r    X    i   v   :    1    1    0    1  .    0    8    9    1   v    1    [   s    t   a    t  .    M    E    ]    5    J   a   n    2    0    1    1 Statistical Science 2010, Vol. 25, No. 3, 289–310 DOI: 10.1214/10-STS330 c Institute of Mathematical Statistics , 2010 To Explain or to Predict? Galit Shmueli Abstract. Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and descrip- tion. In many disciplines there is near-exclusive use of statistical mod- eling for causal explanation and the assumption that models with high explanatory power are inherently of high predictive power. Conflation between exp lanati on and predictio n is common, ye t the dis tinction must be understood for progressing scienti fic knowledge. While this distinction has been recognized in the philosophy of science, the statis- tical 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 re- veal the practical implications of the distinction to each step in the modeling process. Key words and phrases: Explanatory modeling, causality, predictive modeling, predictive power, statistical strategy, data mining, scientific research. 1. INTRODUCTION Looking at how statistical models are used in dif- ferent scientific disciplines for the purpose of the- ory building and testing, one finds a range of per- ceptions regarding the relationship between causal explanation and empirical prediction. In many sci- entific fields such as economics, psychology, educa- tion, and environmental science, statistical models are used almost exclusively for causal explanation, and mode ls tha t poss ess high exp lanato ry pow er are often assumed to inherently possess predictive power. In fields such as natural langua ge processing and bioinforma tics, the focus is on empirical predic- tion with only a slight and indirect relation to causal Galit Shmueli is Associate Professor of Statistics, Department of Decision, Operations and Information Technologies, Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742, USA (e-mail: [email protected] ). This is an electronic reprint of the original article published by the Institute of Mathematical Statistics in Statistical Science, 2010, Vol. 25, No. 3, 289–310. This reprint differs from the original in pagination and typographic detail. explanation. And yet in other research fields, such as epidemiology, the emphasis on causal explanation versus empirical prediction is more mixed. Statisti- cal modeling for description, where the purpose is to capture the data structure parsimoniously, and which is the most commonly developed within the field of statistics, is not commonly used for theory building and testing in other disciplines. Hence, in this ar ticl e I focus on the us e of stat is ti ca l mod- eling for causal explanation and for prediction. My main premise is that the two are often conflated, yet the causal versus predictive distinction has a large impact on each step of the statistical modeling pro- cess and on its consequences. Although not explic- itly stated in the statistics methodology literature, applied statisticians instinctively sense that predict- ing and explaining are different. This article aims to fill a critical void: to tackle the distinction between explanatory modeling and predictive modeling. Clearing the current ambiguity between the two is critical not only for proper statistical modeling, but more importantly, for proper scientific usage. Both explanation and prediction are necessary for gener- ating and testing theories, yet each plays a differ- ent role in doing so. The lack of a clear distinction 1

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http://dx.doi.org/10.1214/10-STS330
http://www.imstat.org/sts/
http://www.imstat.org/
mailto:[email protected]
http://www.imstat.org/
http://dx.doi.org/10.1214/10-STS330
http://www.imstat.org/sts/
http://arxiv.org/abs/1101.0891v1
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