论文标题
通用的燕麦片估计器
The Generalized Oaxaca-Blinder Estimator
论文作者
论文摘要
进行随机实验后，研究人员经常使用普通的至上正方形（OLS）回归来调整基线协变量，以估计平均治疗效果。众所周知，即使线性模型被误指定，所得的置信区间也是有效的。在本文中，我们将结论推广到与非线性模型的协变量调整。我们引入了一种直观的方式来使用任何“简单”非线性模型来构建协变量调整后的置信区间，以达到平均治疗效果。置信区间单独从随机化中得出了其有效性，当非线性模型比线性模型更拟合数据时，它比OLS调整的通常间隔窄。
After performing a randomized experiment, researchers often use ordinary-least squares (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 paper, 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.
