Causal Inference for Incomplete and Heterogeneous Multisite and Blocked Experiments

本研究项目将扩展基于随机的因果推理方法，用于复杂的阻塞和多地点随机对照试验。旨在了解治疗或干预措施的因果效应的研究人员经常使用阻塞和多地点试验。在这些设计中，实验单元被划分为块或场地，然后在每个块或场地内进行独立的实验。这种设计在社会科学和行为科学中尤其常见；然而，目前它们有一些局限性。该项目将通过开发分析复杂阻塞和多地点实验的方法来填补基于随机化的因果推理文献中的许多显着漏洞。这个项目的结果对方法论家和实践者都是有价值的。研究生的大量参与将有助于训练基于随机的因果推理的研究人员。这个项目的材料将用于本科因果推理课程的开发。还将开发免费提供的统计软件包。本研究项目将为复杂的阻塞和多地点实验开发基于随机的因果推理的新方法。当有许多处理时，在每个街区或场地内实施所有处理可能不实际或甚至不可行。使用不完全块设计，在每个块中分配和实施随机子集的处理，可以克服这一挑战，同时保持块设计的结构。该项目将开发一种方法，从基于随机化的潜在结果框架中分析不完整的块设计，这比现有的基于模型的方法需要更少的假设。此外，当有许多感兴趣的因子处理时，可能不是所有块或站点都包含所有因素的完整信息，或者在某些块或站点中可能没有随机化因子的子集。该项目将调查在这些情况下可以确定的因果关系，并将开发推理技术来了解这些影响。除了干预措施的平均因果效应外，人们对了解跨站点异质性也很感兴趣。该项目将探索在什么情况下跨站点的异质性是可识别的，以及如何最好地捕捉这种异质性。对基于随机的推理的关注将使开发的工具与主要使用人类受试者进行实验的社会和行为科学家特别相关，因为基于模型的假设可能不适合收集的数据。了解不同区域或地点的治疗效果异质性也将有助于研究人员评估项目和政策是否应该大规模实施。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。

This research project will expand randomization-based causal inference methodology for complex blocked and multisite randomized control trials. Researchers aiming to learn about the causal effects of treatments or interventions often use blocked and multisite trials. With these designs, experimental units are categorized into blocks or sites and then independent experiments occur within each block or site. The designs are especially common in the social and behavioral sciences; currently, however, they have some limitations. This project will fill many notable holes in the randomization-based causal inference literature by developing methodology for analyzing complex blocked and multisite experiments. The results from this project will be of value to both methodologists and practitioners. The significant involvement of graduate students will aid in the training of researchers in randomization-based causal inference. Materials from this project will be used in the development of an undergraduate causal inference course. Freely available statistical software packages also will be developed.This research project will develop novel methodology for randomization-based causal inference for complex blocked and multisite experiments. When there are many treatments, it may not be practical or even feasible to implement all treatments within each block or site. Use of an incomplete block design, in which a random subset of treatments is assigned and implemented within each block, can overcome this challenge while keeping the structure of the blocked design. The project will develop methodology to analyze incomplete block designs from the randomization-based potential outcome framework which requires fewer assumptions than existing model-based approaches. Further, when there are many factorial treatments of interest, it is possible that not all blocks or sites will contain full information on all factors, or that a subset of factors may not be randomized in some blocks or sites. This project will investigate what causal effects can be identified in these situations and will develop inferential techniques to learn about these effects. In addition to average causal effects of interventions, there is often great interest in learning about cross-site heterogeneity. The project will explore in what settings cross-site heterogeneity is identifiable and how to best capture this heterogeneity. The focus on randomization-based inference will make the tools developed especially relevant to social and behavioral scientists who primarily run experiments using human subjects, for which model-based assumptions may not be appropriate for the data collected. Understanding the amount of treatment effect heterogeneity across blocks or sites also will help researchers assess whether programs and policies should be implemented at a large scale.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.