Robust CATE Estimation Using Novel Ensemble Methods

TL;DR

This paper introduces two novel ensemble methods, Stacked X-Learner and Consensus Based Averaging, to improve the robustness and performance of CATE estimation across diverse scenarios, including complex biological models.

Contribution

The paper proposes two new ensemble techniques that enhance the stability and accuracy of CATE estimators in varied and uncertain data scenarios.

Findings

Both methods outperform existing approaches in diverse scenarios.

The Stacked X-Learner shows superior performance compared to other ensemble methods.

The methods demonstrate robustness in complex biological treatment models.

Abstract

The estimation of Conditional Average Treatment Effects (CATE) is crucial for understanding the heterogeneity of treatment effects in clinical trials. We evaluate the performance of common methods, including causal forests and various meta-learners, across a diverse set of scenarios, revealing that each of the methods struggles in one or more of the tested scenarios. Given the inherent uncertainty of the data-generating process in real-life scenarios, the robustness of a CATE estimator to various scenarios is critical for its reliability. To address this limitation of existing methods, we propose two new ensemble methods that integrate multiple estimators to enhance prediction stability and performance - Stacked X-Learner which uses the X-Learner with model stacking for estimating the nuisance functions, and Consensus Based Averaging (CBA), which averages only the models with highest internal agreement. We show that these models achieve good performance across a wide range of scenarios varying in complexity, sample size and structure of the underlying-mechanism, including a biologically driven model for PD-L1 inhibition pathway for cancer treatment. Furthermore, we demonstrate improved performance by the Stacked X-Learner also when comparing to other ensemble methods, including R-Stacking, Causal-Stacking and others.