用于功能因果推断框架的广义倾向得分加权法
Generalized propensity score weighting for functional causal inference framework
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中文总结 AI 辅助
该研究提出功能倾向得分加权框架，通过对偶优化提升计算效率，可估计功能处理下的因果效应，经UK Biobank数据验证，能准确估计BMI轨迹与糖尿病及代谢指标的因果关系。
中文摘要 AI 辅助
在观察性研究中估计因果效应需要对混杂因素进行调整，当暴露是在连续域上观测到的函数而非标量变量时，这一任务会变得具有挑战性。我们开发了一种功能倾向得分加权框架，通过消除时变处理与观测到的混杂因素之间的依赖关系来实现协变量平衡，从而能够在存在功能处理、协变量和结局的场景中估计边际因果效应。我们提出了权重估计问题的对偶形式，该形式产生了一个平滑的无约束优化问题，提高了计算可扩展性。所提出的框架可自然扩展到时变协变量和纵向结局的场景，通过函数对函数边际结构模型实现因果效应曲面的估计。与现有方法相比，所提方法在协变量平衡、估计准确性和计算效率方面均有所提升，且在扩展到功能协变量和结局时仍能保持这些特性。我们将该方法应用于英国生物银行（UK Biobank）的数据，以估计体重指数轨迹对2型糖尿病风险及后续糖化血红蛋白轨迹的因果效应，其中糖化血红蛋白是一种衡量代谢状态的功能指标。
英文摘要
Estimating causal effects in observational studies requires adjustment for confounding, a task that becomes challenging when the exposure is a function observed over a continuous domain rather than a scalar variable. We develop a functional propensity score weighting framework that achieves covariate balance by removing dependence between time-varying treatments and observed confounders, thereby enabling estimation of marginal causal effects in settings with functional treatments, covariates, and outcomes. We propose a dual formulation of the weight estimation problem that yields a smooth unconstrained optimization and improves computational scalability. The proposed framework extends naturally to settings with time-varying covariates and to longitudinal outcomes via a function-on-function marginal structural model, allowing estimation of causal effect surfaces. The proposed method improves covariate balance, estimation accuracy, and computational efficiency compared to the existing approach and retains these properties when extended to functional covariates and outcomes. We apply the method to data from the UK Biobank to estimate the causal effect of body mass index trajectories on the risk of Type 2 Diabetes and on subsequent glycated hemoglobin trajectories, a functional measure of metabolic status.
