To explain or to predict?

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 ex- planation and the assumption that models with high explanatory power are inherently of high predictive power. Conflation between explanation and pre- diction is common, yet the distinction must be understood for progressing scientific knowledge. While this distinction has been recognized in the phi- losophy of science, the statistical literature lacks a thorough discussion of the many 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 distinction between explanatory and predictive modeling, to discuss its sources, and to reveal the practical implications of the distinction to each step in the modelling process. Abstract from: Statistical Science 2010, Vol. 25, No. 3, 289–310 DOI: 10.1214/10-STS330 © Institute of Mathematical Statistics, 2010 To Explain or to Predict? by Galit Shmueli. Full paper here.