Understanding treatment effect heterogeneity has become an increasingly popular task in various fields, as it helps design personalized advertisements in e-commerce or targeted treatment in biomedical studies. However, most of the existing work in this research area focused on either analyzing observational data based on strong causal assumptions or conducting post hoc analyses of randomized controlled trial data, and there has been limited effort dedicated to the design of randomized experiments specifically for uncovering treatment effect heterogeneity. In the manuscript, we develop a framework for designing and analyzing response adaptive experiments toward better learning treatment effect heterogeneity. Concretely, we provide response adaptive experimental design frameworks that sequentially revise the data collection mechanism according to the accrued evidence during the experiment. Such design strategies allow for the identification of subgroups with the largest treatment effects with enhanced statistical efficiency. The proposed frameworks not only unify adaptive enrichment designs and response-adaptive randomization designs but also complement A/B test designs in e-commerce and randomized trial designs in clinical settings. We demonstrate the merit of our design with theoretical justifications and in simulation studies with synthetic e-commerce and clinical trial data.

翻译：理解处理效应异质性已成为电子商务中设计个性化广告或生物医学研究中实施靶向治疗等众多领域日益重要的任务。然而，该研究领域现有工作大多聚焦于基于强因果假设分析观测数据，或对随机对照试验数据进行事后分析，专门为揭示处理效应异质性而设计的随机实验研究则相对有限。本文提出一个旨在更好地学习处理效应异质性的响应自适应实验设计与分析框架。具体而言，我们构建了响应自适应实验设计框架，能够根据实验过程中积累的证据动态调整数据收集机制。此类设计策略能以更高的统计效率识别具有最大处理效应的亚组。所提框架不仅统一了自适应富集设计与响应自适应随机化设计，还能有效补充电子商务中的A/B测试设计与临床环境中的随机试验设计。我们通过理论论证，并利用合成的电子商务数据与临床试验数据进行模拟研究，验证了所提设计的优越性。