Many practical decision-making problems in economics and healthcare seek to estimate the average treatment effect (ATE) from observational data. The Double/Debiased Machine Learning (DML) is one of the prevalent methods to estimate ATE in the observational study. However, the DML estimators can suffer an error-compounding issue and even give an extreme estimate when the propensity scores are misspecified or very close to 0 or 1. Previous studies have overcome this issue through some empirical tricks such as propensity score trimming, yet none of the existing literature solves this problem from a theoretical standpoint. In this paper, we propose a Robust Causal Learning (RCL) method to offset the deficiencies of the DML estimators. Theoretically, the RCL estimators i) are as consistent and doubly robust as the DML estimators, and ii) can get rid of the error-compounding issue. Empirically, the comprehensive experiments show that i) the RCL estimators give more stable estimations of the causal parameters than the DML estimators, and ii) the RCL estimators outperform the traditional estimators and their variants when applying different machine learning models on both simulation and benchmark datasets.

翻译：经济学和保健领域的许多实际决策问题都试图从观察数据中估计平均治疗效果(ATE) 。双偏/偏差机器学习(DML)是观测研究中评估ATE的常用方法之一。然而,DML估计者可能会遇到一个错误的折射问题,甚至会给出一个极端的估算,而当偏差分被误解或非常接近0或0或1时。 以前的研究通过一些实验技巧,例如偏差分分计分三分,克服了这一问题,但现有文献都没有从理论角度解决这个问题。在本文件中,我们建议采用Robust Causal 学习(RCL) 方法来弥补DML估计者的缺陷。理论上,RCL估计者i(i)与DRL估计者估计者(I)一样,具有一致性和倍增力,因为后者可以通过一些实验技巧,例如偏差分三分,而现有的文献都没有从理论角度来解决这个问题。我们建议采用一种机械学分学习方法,以抵消DL测算师的因果关系参数,而在模型和模型上采用不同的模型时,RCmadregraphist 和模型上采用其模型时,RCmagraphist 。