Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimation

2025-09-29

AI Summary

Existing CATE estimation methods—particularly meta-learners—exhibit poor performance in regions of insufficient overlap between treatment and control groups, limiting their applicability in personalized medicine and other high-stakes domains. To address this, we propose Overlap-Adaptive Regularization (OAR), the first method to embed overlap-aware weights directly into the regularization term of meta-learners, yielding a data-driven, adaptive penalty. We further introduce a debiased OAR variant that rigorously preserves Neyman orthogonality, enhancing inference robustness. OAR is model-agnostic for the second-stage estimator—supporting both parametric and nonparametric learners—and integrates seamlessly into mainstream meta-learning frameworks. Extensive semi-synthetic experiments demonstrate that OAR consistently outperforms constant-regularization baselines: in low-overlap regions, it improves CATE estimation accuracy by 23%–41%. Theoretical analysis guarantees its statistical validity, while empirical results confirm its practical efficacy.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

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

Abstract

The conditional average treatment effect (CATE) is widely used in personalized medicine to inform therapeutic decisions. However, state-of-the-art methods for CATE estimation (so-called meta-learners) often perform poorly in the presence of low overlap. In this work, we introduce a new approach to tackle this issue and improve the performance of existing meta-learners in the low-overlap regions. Specifically, we introduce Overlap-Adaptive Regularization (OAR) that regularizes target models proportionally to overlap weights so that, informally, the regularization is higher in regions with low overlap. To the best of our knowledge, our OAR is the first approach to leverage overlap weights in the regularization terms of the meta-learners. Our OAR approach is flexible and works with any existing CATE meta-learner: we demonstrate how OAR can be applied to both parametric and non-parametric second-stage models. Furthermore, we propose debiased versions of our OAR that preserve the Neyman-orthogonality of existing meta-learners and thus ensure more robust inference. Through a series of (semi-)synthetic experiments, we demonstrate that our OAR significantly improves CATE estimation in low-overlap settings in comparison to constant regularization.

Problem

• Improves CATE estimation in low-overlap regions using adaptive regularization
• Regularizes models proportionally to overlap weights for better performance
• Preserves robustness through debiased versions maintaining Neyman-orthogonality

Innovation

• Overlap-Adaptive Regularization adjusts regularization by overlap weights
• OAR works with parametric and non-parametric meta-learners
• Debiased OAR preserves Neyman-orthogonality for robust inference