CAREER: Adaptive experiments towards learning treatment effect heterogeneity

在许多科学领域，理解和描述不同的和异质的因果效应变得越来越重要。例如，在精准医疗领域，确定不同治疗效果是实现精准医疗效益的重要一步，因为它提供了证据，说明具有特定特征的个体如何对特定治疗产生疗效或不利影响。在社会科学研究中，对不同个体的政府项目或公共政策的有效性进行评估，可以为更有效的决策提供信息。由于可靠设计的随机实验通常为验证治疗或干预的有效性提供最高等级的证据，本研究项目旨在制定三种随机实验设计策略，以更好地了解因果效应异质性。这些设计策略是广泛的，将适用于临床试验、生物医学的社会实验、公共卫生部门和科技企业的在线对照实验。由于本研究将发展现代实验设计策略和具有多种应用的新统计方法，因此本研究项目将提供将研究与教学相结合的机会，并为不同阶段的学生提供培训。该项目将通过培训本科生和研究生以及从代表性不足的群体招募学生进入（生物）统计领域来影响STEM教育。实现这些教育相关目标的活动包括介绍性阅读小组、课程开发、大学本科生研究项目以及针对少数族裔的拓展活动。本研究项目将发展三种新的反应适应性实验设计策略，并从频率论的角度研究治疗效果异质性的理论见解。第一种策略将侧重于设计随机实验，按顺序分配实验努力，以便有效地识别出最受伤害或从特定治疗中受益的亚群。第二种策略将关注学习治疗效果的异质性，通过条件因果效应的变异性来衡量。学习到的异质性允许开发一种新的有效的协变量调整响应自适应框架，其估计器可以达到最佳的可实现效率。第三种策略旨在通过设计随机实验，使参与者的整体福利最大化，从而进一步实现治疗效果异质性的益处。因此，该研究项目有望在适应性实验和社会福利改善之间开辟新的研究联系。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Understanding and characterizing differential and heterogeneous causal effects have become increasingly important in many scientific fields. For example, in precision health, identifying differential treatment effects serves as an essential step towards materializing the benefits of precision health, because it provides evidence regarding how individuals with specific characteristics respond to a given treatment either in efficacy or in adverse effects. In social science research, evaluations of the effectiveness of government programs or public policies across different individuals inform more effective policy-making. As reliably designed randomized experiments often provide evidence of the highest grade for verifying the effectiveness of a treatment or an intervention, this research project aims to develop three randomized experimental design strategies for better learning causal effect heterogeneity. These design strategies are broad and will be applicable in clinical trials, social experiments in biomedical sciences, public health sectors, and online controlled experiments in technological enterprises. Since the project will develop modern experimental design strategies and new statistical methods with many applications, this research project will provide opportunities for integrating research with teaching and training students across different stages. The project will impact STEM education through the training of undergraduate and graduate students and the recruitment of students from underrepresented groups into (bio)statistical fields. Activities to achieve these education-related goals include introductory reading groups, course developments, university undergraduate research programs, and outreach activities to underrepresented minorities.This research project will develop three novel response adaptive experimental design strategies and theoretical insights toward learning treatment effect heterogeneity from a frequentist viewpoint. The first strategy will focus on designing randomized experiments to sequentially allocate experimental efforts so that subpopulations mostly harmed or benefited from a particular treatment can be efficiently identified. The second strategy will focus on learning treatment effect heterogeneity measured by the variability of the conditional causal effect variability. The learned heterogeneity allows the development of a new efficient covariate-adjusted response adaptive framework whose estimator may attain the best achievable efficiency. The third strategy aims to further materialize the benefit of treatment effect heterogeneity by designing randomized experiments to maximize participants' overall welfare. The research project is thus expected to open a new research connection between adaptive experiments and social welfare improvement.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.