The Generalized Oaxaca-Blinder Estimator
本文提出一种方法，在随机实验后使用非线性模型调整协变量来估计平均处理效应，并构造有效的置信区间。当非线性模型拟合数据更好时，该区间比传统OLS调整的区间更窄。
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.