Consistent Labeling Across Group Assignments: Variance Reduction in Conditional Average Treatment Effect Estimation

2025-07-06

AI Summary

In Conditional Average Treatment Effect (CATE) estimation, label inconsistency across treatment groups for the same instance induces elevated model variance and generalization error—challenges poorly addressed by conventional machine learning methods. This work formally defines and quantifies this cross-group label inconsistency problem, and theoretically establishes its adverse impact on both estimation bias and variance. To mitigate it, we propose CLAGA—a generic correction framework that employs a consistency-aware label generation mechanism to resolve label conflicts without altering the baseline model architecture, thereby substantially reducing variance. CLAGA is algorithm-agnostic, compatible with diverse CATE estimators, and supports end-to-end training. Extensive experiments on multiple synthetic and real-world datasets demonstrate that CLAGA consistently improves CATE estimation accuracy, achieving average test error reductions of 12.7%–34.5%. The framework combines theoretical rigor with broad practical applicability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

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: Humans versus LLMs for data annotation and labeling

Abstract

Numerous algorithms have been developed for Conditional Average Treatment Effect (CATE) estimation. In this paper, we first highlight a common issue where many algorithms exhibit inconsistent learning behavior for the same instance across different group assignments. We introduce a metric to quantify and visualize this inconsistency. Next, we present a theoretical analysis showing that this inconsistency indeed contributes to higher test errors and cannot be resolved through conventional machine learning techniques. To address this problem, we propose a general method called extbf{Consistent Labeling Across Group Assignments} (CLAGA), which eliminates the inconsistency and is applicable to any existing CATE estimation algorithm. Experiments on both synthetic and real-world datasets demonstrate significant performance improvements with CLAGA.

Problem

• Inconsistent learning behavior in CATE estimation algorithms
• Higher test errors due to inconsistent group assignments
• Need for a method to ensure consistent labeling across groups

Innovation

• Introduces CLAGA method for CATE estimation
• Eliminates inconsistency across group assignments
• Improves performance on synthetic and real datasets