A Statistical Functional Approach to Estimating Common Treatment Effects in Causal Inference
提出一种统计函数和累积分布函数结构，用于灵活稳健地估计平均处理效应、分位数处理效应等常见因果效应，同时考虑变量选择以识别混杂因素中的信息与网络结构，理论证明变量选择一致性和渐近正态性，数值实验显示该方法优于现有方法。
Estimation of treatment effects is one of the crucial research problems in causal inference, and there are multiple treatment effects depending on the purpose of research targets or researchers’ interests, which include but are not limited to the average treatment effect (ATE) and the quantile treatment effect (QTE). In this study, we aim to propose the statistical functional and cumulative distribution function structure, which leads to a flexible and robust estimator and covers some frequent treatment effects. In addition, our approach also takes variable selection into account, so that informative and network structure in confounders can be identified and implemented in our estimation procedure. The theoretical properties, including variable selection consistency and asymptotic normality of the statistical functional estimator, are established. Some common treatment effects estimations are also conducted in numerical studies, and the results reveal that the proposed estimator generally outperforms the existing methods and is more efficient than its competitors.