To Explain or to Predict?

TL;DR

This paper clarifies the crucial differences between statistical modeling for explanation and prediction, emphasizing their distinct goals, processes, and implications for scientific research.

Contribution

It provides a detailed analysis of the differences between explanatory and predictive modeling, highlighting their practical implications and clarifying common confusions.

Findings

Distinction between explanation and prediction in modeling is essential.

Many models conflating explanation with prediction can hinder scientific progress.

Understanding these differences improves modeling practices and interpretation.

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.