Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data

2025-07-04

AI Summary

This study addresses the challenge of improving estimation accuracy of the conditional average treatment effect (CATE) in randomized controlled trials (RCTs) by leveraging external experimental or observational data—despite distributional discrepancies between external and trial data. To this end, we propose QR-learner, a model-agnostic, doubly robust CATE estimation framework. Theoretically, QR-learner maintains consistency even under covariate shift, and its fusion strategy guarantees performance no worse than RCT-only baselines. It integrates cross-source information via error-minimization and robust learning mechanisms. Extensive simulations and real-data experiments demonstrate that QR-learner significantly reduces mean squared error in CATE prediction and enhances statistical power for detecting treatment effect heterogeneity. By enabling reliable, efficient, and generalizable causal inference, QR-learner provides a principled foundation for precision medicine and individualized treatment decision-making.

Technology Category

• Machine Learning: Causal Learning
• Intelligent Robots: Learning & Optimization for ROB
• Reasoning under Uncertainty: Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Randomized trials are typically designed to detect average treatment effects but often lack the statistical power to uncover effect heterogeneity over patient characteristics, limiting their value for personalized decision-making. To address this, we propose the QR-learner, a model-agnostic learner that estimates conditional average treatment effects (CATE) within the trial population by leveraging external data from other trials or observational studies. The proposed method is robust: it has the potential to reduce the CATE prediction mean squared error while maintaining consistency, even when the external data is not aligned with the trial. Moreover, we introduce a procedure that combines the QR-learner with a trial-only CATE learner and show that it asymptotically matches or exceeds the trial-only learner in terms of mean squared error. We examine the performance of our approach in simulation studies and apply the methods to a real-world dataset, demonstrating improvements in both CATE estimation and statistical power for detecting heterogeneous effects.

Problem

• Estimates heterogeneous treatment effects in trials using external data
• Improves CATE prediction accuracy despite misaligned external data
• Combines QR-learner with trial data for superior performance

Innovation

• QR-learner estimates CATE using external data
• Robust method reduces prediction error consistently
• Combines QR-learner with trial-only learner asymptotically