标题： 稳健的因果学习用于平均处理效应的估计 显示英文标题

标题： Robust Causal Learning for the Estimation of Average Treatment Effects

摘要： 许多经济和医疗保健中的实际决策问题旨在从观察数据中估计平均处理效应（ATE）。双/去偏机器学习（DML）是观测研究中估计ATE的一种流行方法。然而，当倾向得分被错误指定或非常接近0或1时，DML估计量可能会出现误差累积问题，甚至给出极端估计。先前的研究通过一些经验技巧如倾向得分修剪来克服这个问题，但现有的文献中没有从理论角度解决这个问题。在本文中，我们提出了一种稳健因果学习（RCL）方法来弥补DML估计量的不足。理论上，RCL估计量i）与DML估计量一样一致且双重稳健，ii）可以消除误差累积问题。实证上，全面的实验表明i）RCL估计量比DML估计量给出更稳定的因果参数估计，ii）在模拟和基准数据集上应用不同的机器学习模型时，RCL估计量优于传统估计量及其变体。 显示英文摘要

摘要： 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.