To Explain or to Predict?
Bibliographic Data
| ID | 23345353 |
|---|---|
| Authors | Galit Shmueli (0000-0002-0820-0301, University of Maryland, College Park, corresponding author) |
| Year | 2010 |
| Volume | 25 |
| Issue | 3 |
| Publication date | 2010-08-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Statistical Science (JOURNAL) |
| Journal identifiers | ISSN: 0883-4237 • E-ISSN: 2168-8745 |
| Publisher | Institute of Mathematical Statistics (PUBLISHER • US) |
| DOI | 10.1214/10-sts330 |
| OpenAlex | W2951936974 |
| Language | EN |
| Citations received | 282 |
| References cited | 74 |
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.
Causal model · Conflation · Explanatory power · Philosophy of science · Predictive power · Process (computing) · Statistical hypothesis testing · Statistical model · Bayesian Modeling and Causal Inference · Meta-analysis and systematic reviews · Philosophy and History of Science
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| Unique citing works | 282 |
|---|---|
| Citations per year | 18,8 |
| Citation span | 2011 - 2026 (16) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 279 |