Robust Orthogonal Machine Learning of Treatment Effects

TL;DR

This paper introduces a Robust Causal Learning method that improves the stability and accuracy of treatment effect estimation in observational data by addressing the error-compounding issue inherent in existing methods like DML.

Contribution

The paper proposes a theoretically grounded RCL method that satisfies higher-order orthogonal conditions, ensuring consistency, doubly robustness, and elimination of error-compounding in treatment effect estimation.

Findings

RCL estimators provide more stable causal parameter estimates than DML.

RCL outperforms traditional estimators across various machine learning models.

Experimental results include simulations, benchmark datasets, and a synthetic credit dataset.

Abstract

Causal learning is the key to obtaining stable predictions and answering \textit{what if} problems in decision-makings. In causal learning, it is central to seek methods 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. However, the DML estimators can suffer from an \textit{error-compounding issue} and even give extreme estimates when the propensity scores are 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 works solves it from a theoretical standpoint. In this paper, we propose a \textit{Robust Causal Learning (RCL)} method to offset the deficiencies of DML estimators. Theoretically, the RCL estimators i) satisfy the (higher-order) orthogonal condition and are as \textit{consistent and doubly robust} as the DML estimators, and ii) 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 DML; ii) the RCL estimators outperform traditional estimators and their variants when applying different machine learning models on both simulation and benchmark datasets, and a mimic consumer credit dataset generated by WGAN.