Covariance Adjustments for the Analysis of Randomized Field Experiments
Abstract
Background It has become common practice to analyze randomized experiments using linear regression with covariates. Improved precision of treatment effect estimates is the usual motivation. In a series of important articles, David Freedman showed that this approach can be badly flawed. Recent work by Winston Lin offers partial remedies, but important problems remain. Results In this article, we address those problems through a reformulation of the Neyman causal model. We provide a practical estimator and valid standard errors for the average treatment effect. Proper generalizations to well-defined populations can follow. Conclusion In most applications, the use of covariates to improve precision is not worth the trouble.
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Design of Observational Studies
Rosenbaum, P. R. (2010) · Springer Series in Statistics · doi:10.1007/978-1-4419-1213-8 · Cited by 1,150 · see citing works
On regression adjustments to experimental data
Freedman, D. A. (2008) · Advances in Applied Mathematics · doi:10.1016/j.aam.2006.12.003 · Cited by 417 · see citing works
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- Authors
- Berk, R., Pitkin, E., Brown, L., Buja, A., George, E., & Zhao, L.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Berk, Richard
- Venue
- Evaluation Review
- Publisher
- SAGE Publications
- Published
- 2013-06
- Volume
- 37
- Issue
- 3-4
- Pages
- 170-196
- ISSN
- 0145-4692 (ISSN-L)
- 0193-841X
- 1552-3926
- OpenAlex
- W2141258709
- Cited by
- 42
- References
- 2
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 4 · 2023: 3 · 2022: 1 · 2021: 4 · 2020: 5 · 2019: 6 · 2018: 2 · 2017: 2 · 2016: 2 · 2014: 6
- FWCI
- 2.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Evaluation Review (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Evaluation and Performance Assessment
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- randomized field experiment, covariate adjustment, average treatment effect, standard errors, treatment effect estimation
- MeSH terms
- Data Interpretation, Statistical, Humans, Regression Analysis, Models, Statistical, Bias, Causality, Linear Models, Randomized Controlled Trials as Topic
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