Revisiting regression adjustment in experiments with heterogeneous treatment effects
证明在随机抽样下，对控制组和实验组分别进行线性回归调整（FRA）渐近效率不低于简单均值差和混合回归调整，且异质性处理效应时通常更有效；还提出了一类非线性回归调整估计量，即使条件均值函数设定错误也能保证一致性。
In the context of random sampling, we show that linear full (separate) regression adjustment (FRA) on the control and treatment groups is, asymptotically, no less efficient than both the simple difference-in-means estimator and the pooled regression adjustment estimator; with heterogeneous treatment effects, FRA is usually strictly more efficient. We also propose a class of nonlinear regression adjustment estimators where consistency is ensured despite arbitrary misspecification of the conditional mean function. A simulation study confirms that nontrivial efficiency gains are possible with linear FRA, and that further gains are possible, even under severe mean misspecification, using nonlinear FRA.