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.

摘要

在执行随机实验后，研究人员在估计平均治疗效果时经常使用普通最小二乘 (OLS) 回归来调整基线协变量。众所周知，即使线性模型指定错误，所得的置信区间也是有效的。在本文中，我们将该结论推广到非线性模型的协变量调整。我们介绍了一种直观的方法来使用任何“简单”非线性模型来构建平均治疗效果的协变量调整置信区间。置信区间的有效性仅来自随机化，当非线性模型比线性模型更适合数据时，它比通常的 OLS 调整区间窄。