The Generalized Oaxaca-Blinder Estimator

• 成果类型: Article
• 署名作者: Guo, Kevin; Basse, Guillaume
• 署名单位: Stanford University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.1941053
• 发表日期: 2023
• 页码: 524-536
• 关键词: REGRESSION ADJUSTMENTS; Causal Inference; Empirical Processes; MODEL; Robustness; EFFICIENCY; DESIGN; form

摘要

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.