Causal Inference with Irregularly Spaced Observation Times

不规则间隔观察时间的因果推断

• 批准号: 2242776
• 负责人: Shu Yang
• 金额: $ 22.5万
• 依托单位: North Carolina State 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: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242776&HistoricalAwards=false
• 关键词: Causal; Inference; Irregularly; Spaced; Observation

This research project will develop a causal inference and machine learning toolset to tackle important and recurring challenges arising from emergent real-world data. Real-world data (e.g., consumer expenditures, mobile health applications, and electronic health records) provide unique opportunities for discovering optimal treatment strategies for the economy and health care. However, complex data also present novel challenges for statistical analysis. These challenges, such as irregularly spaced observation times or mixed data types, are impediments to effectively translating rich information into meaningful knowledge. This project will result in fundamental, broadly applicable advances in methodology for causal models with complex structures. It will provide principled causal inference approaches to scientific questions with complex data, such as longitudinal observational data, mobile health data, and electronic health records. The results of this research will be incorporated into graduate teaching, short courses, and workshops. Open-source software and R packages also will be developed.This research project will develop simple-to-interpret Marginal Structural Models for multinomial choices, taking into account correlations of expenditure categories, with an application to study the effect of lockdowns on consumer shopping behavior during the COVID-19 pandemic. Semiparametric doubly robust estimators will be developed to address time-varying confounding and irregularly spaced observation times, capitalizing on semiparametric efficiency theory and advanced machine learning methods. The investigator also will develop a unified framework of continuous-time Structural-Nested Models (SNMs) for general outcomes with time-varying confounding and informative observation times. The informativeness of observation times presents vital obstacles to the identification and estimation of the SNM parameter. Finally, electronic health records collect large amounts of granular patient data, which provide both opportunities and challenges for improving the assessment of treatment effects. The investigator will develop causal inference methods for estimating treatment effects with new functional principal component analysis (FPCA) of functional confounders subject to informative sampling for observations. The new FPCA also presents new prospects in the scope of functional data analysis.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.

该研究项目将开发一种因果推理和机器学习工具集，以应对新兴现实世界数据引起的重要和反复出现的挑战。现实世界中的数据（例如，消费者支出，移动健康应用程序和电子健康记录）为发现经济和医疗保健的最佳治疗策略提供了独特的机会。但是，复杂的数据还带来了统计分析的新挑战。这些挑战，例如不规则间隔的观察时间或混合数据类型，是有效地将丰富信息转化为有意义的知识的障碍。该项目将导致具有复杂结构的因果模型的方法论的基本，广泛适用的进步。它将提供有原则的因果推理方法，以使用复杂的数据，例如纵向观察数据，移动健康数据和电子健康记录，以解决科学问题。这项研究的结果将纳入研究生教学，短课程和讲习班中。开源软件和R软件包也将开发。该研究项目将考虑到支出类别的相关性，为多项式选择开发简单的边际结构模型，并应用于研究COVID-19 PANDEMIC期间消费者购物行为的影响。将开发半参数双重稳定的估计器，以解决时变的混杂和不规则间隔的观察时间，利用半参数效率理论和先进的机器学习方法。研究人员还将开发一个统一的连续时间结构性模型（SNM）的统一框架，以使用时变的混杂和信息性观察时间，以进行一般结果。观察时间的信息性为SNM参数的识别和估计带来了重要的障碍。最后，电子健康记录收集了大量的颗粒状患者数据，这既是改善治疗效果评估的机会和挑战。研究者将开发因果推理方法，用于通过新功能主成分分析（FPCA）的功能混杂因素来估算治疗效果，但要通过观测信息提供信息。新的FPCA在功能数据分析范围内还提出了新的前景。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。