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：理解治疗效果异质性已成为各领域中日益重要的任务，因为它有助于在电子商务中设计个性化广告，或在生物医学研究中实施靶向治疗。然而，该领域现有的大多数工作要么基于强因果假设分析观测数据，要么对随机对照试验数据进行事后分析，而专门针对揭示治疗效果异质性设计随机实验的努力十分有限。在本手稿中，我们开发了一个用于设计和分析响应自适应实验的框架，以更好地学习治疗效果异质性。具体而言，我们提出了响应自适应实验设计框架，该框架根据实验期间累积的证据顺序调整数据收集机制。这种设计策略能够以更高的统计效率识别出具有最大治疗效果的亚群。所提出的框架不仅统一了自适应富集设计和响应自适应随机化设计，还补充了电子商务中的A/B测试设计和临床环境中的随机试验设计。我们通过理论证明以及使用合成电子商务和临床试验数据的模拟研究，展示了我们设计的优越性。