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The Generalized Oaxaca-Blinder Estimator

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  • Kevin Guo
  • Guillaume Basse

Abstract

After performing a randomized experiment, researchers often use ordinary least-square (OLS) regression to adjust for baseline covariates when estimating the average treatment effect. It is widely known that the resulting confidence interval is valid even if the linear model is misspecified. In this article, we generalize that conclusion to covariate adjustment with nonlinear models. We introduce an intuitive way to use any “simple” nonlinear model to construct a covariate-adjusted confidence interval for the average treatment effect. The confidence interval derives its validity from randomization alone, and when nonlinear models fit the data better than linear models, it is narrower than the usual interval from OLS adjustment.

Suggested Citation

  • Kevin Guo & Guillaume Basse, 2023. "The Generalized Oaxaca-Blinder Estimator," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(541), pages 524-536, January.
    • Handle: RePEc:taf:jnlasa:v:118:y:2023:i:541:p:524-536
    DOI: 10.1080/01621459.2021.1941053
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    Cited by:

    1. Yujia Gu & Hanzhong Liu & Wei Ma, 2023. "Regression‐based multiple treatment effect estimation under covariate‐adaptive randomization," Biometrics, The International Biometric Society, vol. 79(4), pages 2869-2880, December.
    2. Daniel Ting & Kenneth Hung, 2023. "On the Limits of Regression Adjustment," Papers 2311.17858, arXiv.org.
    3. Zhao, Anqi & Ding, Peng, 2024. "No star is good news: A unified look at rerandomization based on p-values from covariate balance tests," Journal of Econometrics, Elsevier, vol. 241(1).

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