Safe and Robust Causal Inference for High-Dimensional Complex Data

高维复杂数据的安全稳健的因果推理

• 批准号: 2311291
• 负责人: Yang Ning
• 金额: $ 16万
• 依托单位: Cornell University
• 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=2311291&HistoricalAwards=false
• 关键词: Safe; Robust; Causal; Inference; Dimensional

Understanding causal effects among multivariate variables is central to many empirical and experimental types of research. In an era where data is vast, statisticians are faced with the challenge of drawing causal inferences from massive data. For example, the analysis of data can be complicated by the presence of potentially confounding variables, heterogeneity, and temporal dependence within populations. This poses tremendous computational and statistical challenges to existing causal inference methods. Driven by the availability of modern datasets, the research project aims to develop cutting-edge machine-learning methods to address the theoretical, methodological, and computational challenges of drawing causal inferences from massive data. The development of the proposed research would not only push the frontier of causal inference theory but also benefit a broad range of researchers in a variety of areas, including medicine, epidemiology, computer science, and social science. This project also provides research training opportunities for graduate students. The goal of the project is to develop a novel high-dimensional causal inference framework that is influence-function doubly-robust and safe relative to a class of base estimators. Specific projects include proposing a covariate balancing methodology coupled with modern machine learning techniques for efficient causal inference with high-dimensional data, optimally distributed covariate balancing for massive heterogeneous data, and a sequential covariate balancing approach for marginal structural models in longitudinal data. A common theme throughout is the use of the covariate balancing methodology, which comes from the ``propensity score tautology,” the estimated propensity score is appropriate if it balances the covariates.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.

理解多变量之间的因果关系是许多实证和实验类型研究的核心。在一个数据庞大的时代，统计学家面临着从大量数据中得出因果推论的挑战。例如，数据分析可能会因潜在的混杂变量、异质性和种群内的时间依赖性而变得复杂。这对现有的因果推理方法提出了巨大的计算和统计挑战。在现代数据集可用性的推动下，该研究项目旨在开发尖端的机器学习方法，以解决从大量数据中得出因果推论的理论、方法和计算挑战。该研究的发展不仅将推动因果推理理论的前沿，而且将使包括医学、流行病学、计算机科学和社会科学在内的各个领域的广泛研究人员受益。本项目也为研究生提供了研究训练的机会。该项目的目标是开发一种新的高维因果推理框架，该框架相对于一类基估计量具有影响函数双鲁棒性和安全性。具体项目包括提出一种协变量平衡方法，结合现代机器学习技术，对高维数据进行有效的因果推理，对大量异构数据进行最佳分布协变量平衡，以及对纵向数据中的边缘结构模型进行顺序协变量平衡方法。贯穿始终的一个共同主题是协变量平衡方法的使用，它来自“倾向分数同义反复”，如果估计的倾向分数平衡了协变量，那么它就是合适的。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。