Matched-Pair Experimental Design with Active Learning
This work addresses the challenge of efficiently identifying effective interventions in clinical or social experiments, though it appears incremental as it adapts active learning to an existing matched-pair design.
The paper tackles the problem of detecting treatment effects in matched-pair experimental designs when overall effect sizes are small, by proposing an active learning framework that sequentially enrolls patients in high treatment-effect regions, resulting in reduced experimental costs and ensured coverage of these regions.
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.