CAREER: Advances in Randomization Inference for Causal Effects: Heterogeneity, Sensitivity, and Complexity

了解因果效应在各种社会、生物医学和工业研究中具有重要意义，因为它在决策和政策制定中发挥着至关重要的作用。该项目旨在创建创新的统计方法，以更全面地了解因果关系的异质性，更可靠地评估观察研究中因果结论对未测量混杂的敏感性，并为现代复杂实验提供可靠的推断。这项研究有可能回答政治学、教育学和社会学等不同学科的问题。例如，除了通常的平均治疗效果外，该项目还可以帮助解决有关在任何程度上受益于特定政策的个人比例的询问。国际和平研究所打算通过出版物、演讲和分发开放源码软件来传播研究成果。此外，教育和推广活动将被系统地整合到研究议程中，旨在加强本科教育，向更广泛的受众传播因果关系知识，并使研究生掌握关键技能，使他们能够成为深入的研究人员和以人为中心的教育者。首席调查员计划开发新的工具，在随机实验和观察性研究中提供对因果关系的更全面和更有力的理解。这些工具将建立在随机化推理的基础上或受到随机化推理的启发，随机化推理使用治疗分配的随机化作为合理的基础。该项目有三个主要目标。首先，PI将为个体因果效应的分布开发推理技术，这在实践中是一个重要的问题，但由于其无法从观测数据中识别而难以推断。其次，该项目将提供新的敏感性分析，可以适应观察性研究中的极端隐藏混淆，这可以加强因果结论。第三，PI将为复杂的随机实验开发稳健的推理方法，这些实验超越了简单的随机化或涉及同行的影响。最后，该项目将提供新的计算效率高的算法，并将创建公开可用的R软件包，以促进这些新工具在应用程序中的使用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Understanding causal effects holds significant importance in various social, biomedical, and industrial studies, as it plays a vital role in decision making and policy formulation. This project aims to create innovative statistical methodologies that provide a more comprehensive understanding of causal effect heterogeneity, a more reliable assessment of the sensitivity of causal conclusions to unmeasured confounding in observational studies, and robust inference for modern complex experiments. The research has the potential to answer questions in such a diverse set of disciplines, as political science, education, and sociology. For instance, the project can help address inquiries regarding the proportion of individuals who benefit from a specific policy to any extent, in addition to the usual average treatment effects. The PI intends to disseminate the research outputs through publications, presentations, and the distribution of open-source software. Additionally, the educational and outreach activities will be systematically integrated to the research agenda, aiming to enhance undergraduate education, spread causality knowledge to the broader audiences, and equip graduate students with the critical skills allowing them to become in-depth researchers and human-centered educators.The Principal Investigator plans to develop new tools that provide a more comprehensive and robust understanding of causal effects in both randomized experiments and observational studies. These tools will be built upon or inspired by the randomization inference, which uses the randomization of treatment assignments as the reasoned basis. The project has three primary objectives. First, the PI will develop inference techniques for the distribution of individual causal effects, which is an important concern in practice, yet difficult to infer due to its unidentifiability from the observed data. Second, the project will deliver new sensitivity analyses that can accommodate extreme hidden confounding in observational studies, which can strengthen the causal conclusions. Third, the PI will develop robust inference methods for complex randomized experiments that go beyond simple randomization or involve peer influence. Finally, the project will provide new computationally efficient algorithms and will create publicly available R software packages that will facilitate the use of these new tools in applications.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.