Matched-Pair Experimental Design with Active Learning
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.
Cite
@article{arxiv.2509.10742,
title = {Matched-Pair Experimental Design with Active Learning},
author = {Weizhi Li and Gautam Dasarathy and Visar Berisha},
journal= {arXiv preprint arXiv:2509.10742},
year = {2025}
}