CAREER: CCF: CIF: Randomized Experimentation for Systems with Time-varying Dynamics and Network Interference

职业：CCF：CIF：具有时变动态和网络干扰的系统的随机实验

• 批准号: 2337796
• 负责人: Christina Yu
• 金额: $ 59.54万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2029-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2337796&HistoricalAwards=false
• 关键词: CAREER; CCF; CIF; Randomized; Experimentation

Randomized experiments are widely used to estimate causal effects of proposed treatments in a range of domains, including the natural and social sciences, engineering, health care, and the tech industry. However, many modern systems involve complex dependencies across individuals and time, referred to as interference. The interference violates assumptions that are critical to the performance of conventional causal estimators. Without adjusting for interference, existing approaches can result in incorrect conclusions that have detrimental downstream impact on policy decisions. This project focuses on developing a new toolkit of methods for causal inference that address interference arising from network interactions and time dynamics. The project will develop a causal inference software package that will be used as a vehicle to integrate the research into education and outreach to pre-college students, undergraduate student researchers, and industry collaborators.The project will consider settings where the dependencies arise from direct impact of the treatments of neighbors on the outcome of an individual, as well as settings where the impacts are mediated through state variables such that the treatment effect may propagate across the network over time. The investigator will explore different modeling assumptions to facilitate estimation given the complexities of interference, while aiming for robust solutions and analyses that can be generalized to broad classes of models. The developed techniques will illuminate the implications of known structure and richness of available measurements on the feasibility of estimation by exploiting the interaction between model assumptions, the choice of the randomized experimental design and estimator, and achievable guarantees. The educational component of the project will also involve building a software package that enables individuals and researchers to simulate randomized experiments and observational studies across various applications to benchmark new solution concepts.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.

随机实验被广泛用于估计拟议治疗的因果关系，包括自然和社会科学，工程，医疗保健和科技行业。但是，许多现代系统涉及个人和时间之间的复杂依赖性，称为干扰。干扰违反了对常规因果估计量至关重要的假设。在不调整干扰的情况下，现有方法可能会导致不正确的结论，这些结论对政策决策有不利影响。该项目着重于开发一种因果推理方法的新工具包，该方法解决了由网络交互和时间动态引起的干扰。 该项目将开发一个因果推理软件包，该软件包将用作工具，将研究和宣传的研究整合到前大学的学生，本科生研究人员和行业合作者中。该项目将考虑依赖关系在邻居对个人的预期以及对现场的影响范围内的范围造成的依赖性的直接影响，以及该范围的范围越来越多。考虑到干扰的复杂性，研究者将探索不同的建模假设，以促进估计，同时旨在实现可靠的解决方案和分析，这些解决方案和分析可以推广到广泛的模型类别。开发的技术将通过利用模型假设之间的相互作用，随机实验设计和估算器的选择以及可实现的保证来阐明已知结构和可用测量对估计可行性的含义。该项目的教育部分还将涉及建立一个软件包，使个人和研究人员能够模拟各种应用程序的随机实验和观察性研究，以基于新的解决方案概念。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛的影响来通过评估来支持的，并具有更广泛的影响。