主动学习匹配对设计，高效定位治疗效果显著人群。
Matched-Pair Experimental Design with Active Learning
- 将目标区域识别转为分类问题，用主动学习动态选人。
- 实验成本更低，且能完整覆盖高疗效人群区域。
- 适合小效应场景下精准医疗的试验设计。
匹配对实验设计通过配对参与者并比较对内结果差异来检测干预效果。当总体效应较小时，研究重点转向识别和聚焦治疗效果高的区域。本文提出一种序列化主动招募策略，持续将患者纳入高治疗效应区域。重要的是，我们将目标区域识别建模为分类问题，并设计了适配匹配对设计的主动学习框架。该方法不仅降低检测治疗有效性的实验成本，还能确保识别出的区域包含全部高治疗效应区域。理论分析显示该框架具有较低标签复杂度，实际场景实验验证了其高效性与优势。
原文摘要 · Abstract (English)
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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