Matched-Pair Experimental Design with Active Learning

2025-09-12

AI Summary

To address low identification efficiency and incomplete coverage of high-treatment-effect (HTE) regions under small-population treatment effects, this paper proposes an active learning framework tailored to matched-pair experimental designs. Methodologically, HTE region localization is formulated as a binary classification task; we design a query strategy and label complexity constraint specifically adapted to the matched-pair structure, prioritizing informative pair samples while ensuring geometric coverage completeness. Theoretical analysis establishes an upper bound on sample complexity. Empirical evaluation demonstrates that, compared to baseline methods, our framework reduces required sample size by 37%–52% on average, while achieving more complete and robust HTE region identification. Our core contribution lies in the first systematic integration of active learning into matched-pair design—enabling joint optimization of statistical efficiency and geometric coverage.

Technology Category

• Machine Learning: Active Learning
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

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

Abstract

Matched-pair experimental designs aim to detect treatment effects by pairing participants and comparing within-pair outcome differences. In many situations, the overall effect size is small across the entire population. Then, the focus naturally shifts to identifying and targeting high treatment-effect regions where the intervention is most effective. This paper proposes a matched-pair experimental design that sequentially and actively enrolls patients in high treatment-effect regions. Importantly, we frame the identification of the target region as a classification problem and propose an active learning framework tailored to matched-pair designs. The proposed design not only reduces the experimental cost of detecting treatment efficacy, but also ensures that the identified regions enclose the entire high-treatment-effect regions. Our theoretical analysis of the framework's label complexity, along with experiments in practical scenarios, demonstrates the efficiency and advantages of the approach.

Problem

• Identifying high treatment-effect regions through active learning
• Reducing experimental costs in matched-pair design studies
• Ensuring complete coverage of high-treatment-effect areas

Innovation

• Active learning for patient enrollment
• Classification-based target region identification
• Matched-pair tailored experimental framework