Doubly robust uniform confidence band for the conditional average treatment effect function
提出一种双重稳健方法估计平均处理效应随协变量变化的异质性，适用于高维协变量但关注低维异质性的场景，并给出易计算的统一置信带，通过蒙特卡洛实验和吸烟对出生体重影响的应用验证其有效性。
Summary In this paper, we propose a doubly robust method to estimate the heterogeneity of the average treatment effect with respect to observed covariates of interest. We consider a situation where a large number of covariates are needed for identifying the average treatment effect but the covariates of interest for analyzing heterogeneity are of much lower dimension. Our proposed estimator is doubly robust and avoids the curse of dimensionality. We propose a uniform confidence band that is easy to compute, and we illustrate its usefulness via Monte Carlo experiments and an application to the effects of smoking on birth weights.