On regression adjustments to experimental data
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In RCTs, complex regression models with many covariates may inflate standard errors and bias estimates.
“In RCTs, complex regression models with a series of covariates are not only overly complicated but may, in fact, inflate standard errors and bias estimates (Berk et al., 2014;Freedman, 2008).”
From Randomized controlled trial of social interaction police training (2020) · cites this paper as Freedman, 2008
The Introduction reports that regression adjustment can bias estimates and that conventionally calculated standard errors can be severely biased; the Abstract says adjustment may worsen precision. However, the paper’s comparison uses one covariate and does not establish that having many covariates or model complexity causes these problems. (LLM, conf=0.86)
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- Authors
- Freedman, D. A.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Freedman, David A.
- Venue
- Advances in Applied Mathematics
- Publisher
- Elsevier BV
- Published
- 2008-02
- Volume
- 40
- Issue
- 2
- Pages
- 180-193
- ISSN
- 0196-8858 (ISSN-L)
- OpenAlex
- W2148472246
- Cited by
- 417
- References
- 27
- Citations by year
- 2026: 25 · 2025: 48 · 2024: 30 · 2023: 20 · 2022: 23 · 2021: 37 · 2020: 39 · 2019: 21 · 2018: 21 · 2017: 13 · 2016: 15 · 2015: 15 · 2014: 17 · 2013: 19 · 2012: 12
- FWCI
- 12.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Advances in Applied Mathematics (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- regression adjustment, randomized experiments, potential outcomes, standard error estimation, asymptotic expansion, causal inference
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