To Explain or to Predict?
Top Cited Papers
Open Access
- 1 August 2010
- journal article
- research article
- Published by Institute of Mathematical Statistics in Statistical Science
- Vol. 25 (3), 289-310
- https://doi.org/10.1214/10-sts330
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.Keywords
This publication has 76 references indexed in Scilit:
- Predictive Analytics in Information Systems ResearchSSRN Electronic Journal, 2010
- Does depression predict coronary heart disease and cerebrovascular disease equally well? The Health and Social Support Prospective Cohort StudyInternational Journal of Epidemiology, 2010
- Real-Time Forecasting of Online Auctions via Functional K-Nearest NeighborsSSRN Electronic Journal, 2009
- Assessing biodiversity by remote sensing in mountainous terrain: the potential of LiDAR to predict forest beetle assemblagesJournal of Applied Ecology, 2009
- Bayes not Bust! Why Simplicity is no Problem for BayesiansThe British Journal for the Philosophy of Science, 2007
- A Comprehensive Look at The Empirical Performance of Equity Premium PredictionThe Review of Financial Studies, 2007
- Predictive Accuracy as an Achievable Goal of SciencePhilosophy of Science, 2002
- The central role of the propensity score in observational studies for causal effectsBiometrika, 1983
- The Predictive Sample Reuse Method with ApplicationsJournal of the American Statistical Association, 1975
- Investigating Causal Relations by Econometric Models and Cross-spectral MethodsEconometrica, 1969