Robust Causal Learning for the Estimation of Average Treatment Effects

--Many practical decision-making problems in economics and healthcare seek to estimate the average treatment effect (A TE) from observational data. The Double/Debiased Machine Learning (DML) is one of the prevalent methods to estimate A TE 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. Causal inference is ubiquitous for decision-making problems in various areas such as Healthcare [1]-[3] and Economics [4]-[6]. At the core of causal machine learning, estimating the average treatment effect (A TE) from observational data is challenging because some features (covariates) can influence both treatment and outcome in most practical circumstances. Co-first authors are in alphabetical order. Qi Wu is the corresponding author. Shumin Ma is also with Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College. To obtain a clean A TE, one can conduct the Randomized Controlled Trials (RCTs). RCTs are regarded as the gold standard to evaluate A TE, whereas conducting RCTs is often expensive and time-consuming.