In this paper, we introduce a unified estimator to analyze various treatment effects in causal inference, including but not limited to the average treatment effect (ATE) and the quantile treatment effect (QTE). The proposed estimator is developed under 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 be implemented in our estimation procedure. The theoretical properties, including variable selection consistency and asymptotic normality of the statistical functional estimator, are established. Various 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.

翻译：本文提出了一种统一估计量，用于分析因果推断中的多种处理效应，包括但不限于平均处理效应（ATE）和分位数处理效应（QTE）。所提出的估计量是在统计泛函与累积分布函数的结构下构建的，从而得到一个灵活且稳健的估计量，并涵盖了一些常见的处理效应。此外，我们的方法还考虑了变量选择，使得混杂因素中的信息性及网络结构能够被识别，并在估计过程中得以实施。我们建立了该统计泛函估计量的理论性质，包括变量选择一致性及渐近正态性。数值研究中也进行了多种处理效应的估计，结果表明所提出的估计量通常优于现有方法，且比同类方法更为高效。