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arXiv · 2509.10742

Matched-Pair Experimental Design with Active Learning

Weizhi Li·Gautam Dasarathy·Visar Berisha
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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 across the entire population is small. 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. Our 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 and experiments in practical scenarios demonstrate the efficiency and advantages of the approach.

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Weizhi Li, Gautam Dasarathy, Visar Berisha. 2025-09-12. Matched-Pair Experimental Design with Active Learning. https://arxiv.org/abs/2509.10742

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