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CAREER: Robust Matching Algorithms for Causal Inference in Large Observational Studies

职业：大型观察研究中因果推理的稳健匹配算法

基本信息

批准号：
2047094
负责人：
MD NOOR E ALAM
金额：
$ 50.02万
依托单位：
Northeastern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2026-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047094&HistoricalAwards=false
关键词：
CAREER Robust Matching Algorithms Causal

项目摘要

This Faculty Early Career Development Program (CAREER) grant will advance the national health, prosperity, and economic welfare by utilizing the power of big data to infer causality in large-scale observational studies. In many situations, particularly in the public health domain, it may be difficult or prohibitively expensive to design controlled studies to evaluate effective public policies. As large-scale data collection increases, the design of methods to infer causality between treatment and outcome by partitioning observations into appropriate sets has become an attractive alternative. Current methods underlying causal inference suffer from several fundamental challenges that may lead to sub-optimal policy selection. This project will develop tractable computational approaches to facilitate better policy decision making. As an important use case, the project will evaluate policies for improving treatment quality of Opioid Use Disorder (OUD) using large-scale U.S. healthcare data. The integrated education and research plan will attract and involve a diverse student body, from high school through graduate school, in research and practice. Through active engagement with partnering organizations, including community colleges and an HBCU, the project will provide opportunities for members of underrepresented groups in engineering to address pressing societal needs. Using a modern optimization perspective, this project will advance existing methods for causal inference by developing a theoretical and computational framework that encompasses both inference and matching to identify causality from an observational study. The research objectives are to (1) establish a robust causal inference framework with matching methods to reduce uncertainty, (2) ensure covariate balance in high dimensional space, (3) develop optimal covariate balance techniques to reduce bias and model dependency by ensuring desired distributional properties, and (4) evaluate and advance U.S. healthcare policies based on this framework. To this end, a rigorous optimization framework will be employed to explicitly account for uncertainties in causal inference, maintain neighborhood structures of high dimensional data in low dimensions with matching requirements, and ensure optimal distributional properties of observational data. Efficient exact solution algorithms will be developed exploiting problem structure. Scalability will be addressed through algorithmic schemes with desirable convergence properties and data structure-based decomposition methods. These algorithms are expected to be useful to a wide variety of optimization problems such as quadratic assignment, convex-nonlinear feasibility, and binary feasibility.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.

该教师早期职业发展计划（CAREER）拨款将通过利用大数据的力量来推断大规模观察性研究中的因果关系，促进国家健康，繁荣和经济福利。在许多情况下，特别是在公共卫生领域，设计对照研究来评估有效的公共政策可能很困难或费用过高。随着大规模数据收集的增加，通过将观察结果划分为适当的集合来推断治疗和结果之间的因果关系的方法的设计已成为一种有吸引力的替代方案。目前的因果推理方法受到几个基本的挑战，可能会导致次优的政策选择。这个项目将开发易于处理的计算方法，以促进更好的政策决策。作为一个重要的用例，该项目将使用大规模的美国医疗保健数据评估改善阿片类药物使用障碍（OUD）治疗质量的政策。综合教育和研究计划将吸引和参与多样化的学生团体，从高中到研究生院，在研究和实践。通过与社区学院和HBCU等合作组织的积极参与，该项目将为工程领域代表性不足的群体成员提供机会，以满足紧迫的社会需求。使用现代优化的角度来看，该项目将通过开发一个理论和计算框架，包括推理和匹配，以确定从观察研究的因果关系，推进现有的因果推理方法。研究目标是（1）建立一个强大的因果推理框架，匹配方法，以减少不确定性，（2）确保协变量在高维空间的平衡，（3）开发最佳的协变量平衡技术，以减少偏见和模型的依赖性，通过确保所需的分布特性，以及（4）评估和推进美国医疗保健政策的基础上，这个框架。为此，将采用严格的优化框架来明确解释因果推理中的不确定性，在低维中保持高维数据的邻域结构与匹配要求，并确保观测数据的最佳分布特性。有效的精确解算法将开发利用问题的结构。可扩展性将通过具有理想收敛特性的算法方案和基于数据结构的分解方法来解决。这些算法预计将是有用的各种各样的优化问题，如二次分配，凸非线性可行性，和二元可行性。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。