Causal Inference for Incomplete and Heterogeneous Multisite and Blocked Experiments

不完整、异构的多站点和分块实验的因果推断

• 批准号: 2316908
• 负责人: Nicole Pashley
• 金额: $ 35万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316908&HistoricalAwards=false
• 关键词: Causal; Inference; Incomplete; Heterogeneous; Multisite

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.

本研究计画将扩展以随机化为基础的因果推论方法，应用于复杂的区组及多中心随机对照试验。旨在了解治疗或干预的因果效应的研究人员经常使用封闭和多中心试验。在这些设计中，实验单元被分类为区块或地点，然后在每个区块或地点内进行独立的实验。这种设计在社会和行为科学中特别常见;然而，目前它们有一些局限性。这个项目将填补许多显着的漏洞，在随机化为基础的因果推理文献，通过开发方法来分析复杂的块和多地点的实验。这个项目的结果将对方法学家和实践者都有价值。研究生的重要参与将有助于培训研究人员在随机化为基础的因果推理。这个项目的材料将用于发展本科因果推理课程。本研究计划将为复杂的区组和多地点实验开发基于随机化的因果推断的新方法。当有许多处理方法时，在每个区块或地点内实施所有处理方法可能不实际，甚至不可行。使用不完全区组设计，其中在每个区组内分配和实施随机治疗子集，可以克服这一挑战，同时保持区组设计的结构。该项目将开发方法，从基于随机化的潜在结果框架中分析不完整区组设计，该框架比现有的基于模型的方法需要更少的假设。此外，当有许多相关因子治疗时，可能并非所有区组或研究中心都包含所有因子的完整信息，或者某些区组或研究中心的因子子集可能未随机化。这个项目将调查在这些情况下可以确定什么样的因果关系，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，人们往往对了解跨地点异质性有很大的兴趣。该项目将探讨在什么样的设置跨站点异质性是可识别的，以及如何最好地捕捉这种异质性。对基于随机化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。了解不同区块或地点的治疗效果异质性也将有助于研究人员评估是否应该大规模实施项目和政策。该奖项反映了NSF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。