论文标题
复杂功能纵向研究的因果鉴定
Causal Identification for Complex Functional Longitudinal Studies
论文作者
论文摘要
现代医学研究中的实时监测引入了功能纵向数据，其特征在于结果，治疗和混杂因素的连续时间测量。这种复杂性导致无限的无限治疗困扰者反馈，传统的因果推理方法无法处理。受到粗糙的数据框架的启发，我们采用随机过程理论，测量理论和净收敛来提出非参数因果鉴定框架。该框架概括了经典的G形成，逆概率加权和双重稳健的公式，可容纳时间变化的结果，但受到功能纵向数据的死亡率和审查。我们通过蒙特卡洛模拟检查我们的框架。我们的方法解决了当前方法中的显着差距，为功能纵向数据提供了解决方案，并为该领域的未来估计工作铺平了道路。
Real-time monitoring in modern medical research introduces functional longitudinal data, characterized by continuous-time measurements of outcomes, treatments, and confounders. This complexity leads to uncountably infinite treatment-confounder feedbacks, which traditional causal inference methodologies cannot handle. Inspired by the coarsened data framework, we adopt stochastic process theory, measure theory, and net convergence to propose a nonparametric causal identification framework. This framework generalizes classical g-computation, inverse probability weighting, and doubly robust formulas, accommodating time-varying outcomes subject to mortality and censoring for functional longitudinal data. We examine our framework through Monte Carlo simulations. Our approach addresses significant gaps in current methodologies, providing a solution for functional longitudinal data and paving the way for future estimation work in this domain.
