Causal Inference with Irregularly Spaced Observation Times

该研究项目将开发一个因果推理和机器学习工具集，以解决现实世界数据中出现的重要和反复出现的挑战。真实世界数据（例如，消费者支出、移动的健康应用和电子健康记录）为发现经济和健康护理的最佳治疗策略提供了独特的机会。然而，复杂的数据也给统计分析带来了新的挑战。这些挑战，例如不规则间隔的观察时间或混合的数据类型，是有效地将丰富的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方法的根本性，广泛适用的进步。它将为复杂数据的科学问题提供原则性的因果推理方法，如纵向观察数据，移动的健康数据和电子健康记录。这项研究的结果将纳入研究生教学，短期课程和研讨会。该研究项目将开发易于解释的边际结构模型（Marginal Structural Models），用于多项选择，考虑支出类别的相关性，并应用于研究COVID-19疫情期间封锁对消费者购物行为的影响。将开发半参数双重稳健估计器，以解决时变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。研究者还将开发一个统一的连续时间结构嵌套模型（SNM）框架，用于具有时变混杂和信息观测时间的一般结局。观测时间的信息量对SNM参数的识别和估计提出了重要的障碍。最后，电子健康记录收集了大量精细的患者数据，这为改善治疗效果评估提供了机遇和挑战。研究者将开发因果推断方法，通过对功能性混杂因素进行新的功能性主成分分析（FPCA）来估计治疗效果，并进行信息性抽样观察。新的FPCA还展示了功能数据分析领域的新前景。该奖项反映了NSF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。

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.