To Explain or to Predict?

TLDR

Statistical modeling is widely used for causal explanation, prediction, and description, yet the distinction between explanatory and predictive modeling is often conflated, and this paper notes that while philosophy recognizes the difference, the statistical literature lacks a thorough discussion of the differences that arise in modeling for explanatory versus predictive goals. The article aims to clarify the distinction between explanatory and predictive modeling, discuss its sources, and reveal practical implications for each step in the modeling process. The authors analyze the sources of the distinction and outline its practical implications for each step in the modeling process.

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.