To Explain or to Predict?

Statistical Science2010, Vol. 25, No. 3, 289 310 Institute of Mathematical Statistics, 2010To Explain or to Predict? Galit modeling is a powerful tool for developing and testingtheories by way of causal explanation, prediction, and description. In manydisciplines there is near-exclusive use of statistical modeling for causal ex-planation and the assumption that models with high explanatory power areinherently of high predictive power. Conflation between explanation and pre-diction is common, yet the distinction must be understood for progressingscientific knowledge. While this distinction has been recognized in the phi-losophy of science, the statistical literature lacks a thorough discussion of themany differences that arise in the process of modeling for an explanatory ver-sus a predictive goal. The purpose of this article is to clarify the distinctionbetween explanatory and predictive modeling, to discuss its sources, and toreveal the practical implications of the distinction to each step in the model-ing words and phrases:Explanatory modeling, causality, predictive mod-eling, predictive power, statistical strategy, data mining, scientific INTRODUCTIONL ooking at how statistical models are used in dif-ferent scientific disciplines for the purpose of theorybuilding and testing, one finds a range of perceptionsregarding the relationship between caus

for the purpose of predicting new or future observa-tions. In particular, I focus on nonstochastic prediction (Geisser, 1993, page 31), where the goal is to predict the output value (Y) for new observations given their input values (X). This definition also includes temporal forecasting, where observations until time t (the input)

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