CAREER: Statistical Inference in Observational Studies -- Theory, Methods, and Beyond

职业：观察研究中的统计推断——理论、方法及其他

• 批准号: 2338760
• 负责人: Rajarshi Mukherjee
• 金额: $ 45万
• 依托单位: Harvard 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=2338760&HistoricalAwards=false
• 关键词: CAREER; Statistical; Inference; Observational; Studies

Causal inference refers to a systematic way of deciphering causal relationships between entities from empirical observations – an epistemic framework that underlies past, present, and future scientific and social development. For designing statistical methods for causal inference, the gold standard pertains to randomized clinical trials where the researcher assigns treatment/exposure to subjects under study based on pure chance mechanisms. The random assignment negates systematic bias between the observed relationship between the treatment/exposure and outcome due to unknown common factors referred to as confounders. However, randomized clinical trials are often infeasible, expensive, and ethically challenging. In contrast, modern technological advancement has paved the way for the collection of massive amounts of data across a spectrum of possibilities such as health outcomes, environmental pollution, medical claims, educational policy interventions, and genetic mutations among many others. Since accounting for confounders in such data is the fundamental aspect of conducting valid causal inference, one of the major foci of modern causal inference research have been to design procedures to account for complex confounding structures without pre-specifying unrealistic statistical models. Despite the existence of a large canvas of methods in this discourse, the complete picture of the best statistical methods for inferring the causal effect of an exposure on an outcome while adjusting for arbitrary confounders remains largely open. Moreover, there are several popularly used methods that require rigorous theoretical justification and subsequent modification for reproducible statistical research in the domain of causal inference. This project is motivated by addressing these gaps and will be divided into two broad interconnected themes. In the first part, this project provides the first rigorous theoretical lens to the most popular method of confounder adjustment in large-scale genetic studies to find causal variants of diseases. This will in turn bring forth deeper questions about optimal statistical causal inference procedures that will be explored in the second part of the project. Since the project is designed to connect ideas from across statistical methods, probability theory, computer science, and machine learning, it will provide unique learning opportunities to design new courses and discourses. The project will therefore integrate research with education through course development, research mentoring for undergraduate and graduate students, especially those from underrepresented groups, and summer programs.This project will focus on two broad and interrelated themes tied together by the motivation of conducting statistical and causal inference with modern observational data. The first part of the project involves providing the first detailed theoretical picture of the most popular principal component-based method of population stratification adjustment in genome-wide association studies. This part of the project also aims to provide new methodologies to correct for existing and previously unknown possible biases in the existing methodology as well as guidelines for practitioners for choosing between methods and design of studies. By recognizing the fundamental tenet of large-scale genetic data analysis as the identification of causal genetic determinants of disease phenotypes, the second part of the project develops the first complete picture of optimal statistical inference of causal effects in both high-dimensional under sparsity and nonparametric models under smoothness conditions. Moreover, this part of the project responds to the fundamental question of tuning learning algorithms for estimating nuisance functions, such as outcome regression and propensity score for causal effect estimation, to optimize the downstream mean-squared error of causal effect estimates instead of prediction errors associated with these regression functions. The overall research will connect ideas from high-dimensional statistical inference, random matrix theory, higher-order semiparametric methods, and information theory.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.

因果关系是从经验观察中解密的一种系统性的方法，这是对过去，现在和未来的科学发展的基础。纯粹的变化机制。在诸如健康结果，环境污染，医疗要求和遗传突变之类的诸如其他许多方面的研究中，大量的数据收集了大量的数据最佳策略论的前肢的存在是在很大程度上开放的XPOSIL效果，以调整任意混杂因素。在大规模研究中调整有关因果关系的最佳因果关系。将跨统计方法，概率理论，计算机科学和机器学习社会的想法联系起来，以设计新课程和话语。现代观察数据的统计和因果关系。通过大规模遗传数据分析的基本宗旨，作为疾病表型的鉴定，在研究的基本宗旨中，在研究中选择了甲基甲基甲基苯丙胺的可能性，并为实践者选择。在稀疏性和非参数模型下，在平滑度条件下的最佳地位。与这些传播功能相关的预测，RCH将连接高维统计学的想法，随机矩阵理论，高级掌握方法。该奖项反映了NSF的法定任务，并认为值得支持基金会的知识分子和影响审阅者IA。