The Generalized Oaxaca-Blinder Estimator. Issue 541 (2nd January 2023)

• Record Type: Journal Article
• Authors: Guo, Kevin; Basse, Guillaume
• Is Part Of: Journal of the American Statistical Association. Volume 118:Issue 541(2023)
• Page Start: 524
• Page End: 536
• Publication Date: 2023-01-02
• Subjects: Agnostic covariate adjustment -- Neyman model -- Randomization inference; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2021.1941053
• Languages: English
• ISSNs: 0162-1459

Abstract

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.