Existing causal methods for time-varying exposure and time-varying confounding focus on estimating the average causal effect of a time-varying binary treatment on an end-of-study outcome, offering limited tools for characterizing marginal causal dose-response relationships under continuous exposures. We propose a scalable, nonparametric Bayesian framework for estimating marginal longitudinal causal dose-response functions with repeated outcome measurements. Our approach targets the average potential outcome at any fixed dose level and accommodates time-varying confounding through the generalized propensity score. The proposed approach embeds a Dirichlet process specification within a generalized estimating equations structure, capturing temporal correlation while making minimal assumptions about the functional form of the continuous exposure. We apply the proposed methods to monthly metro ridership and COVID-19 case data from major international cities, identifying causal relationships and the dose-response patterns between higher ridership and increased case counts.

翻译：现有针对时变暴露和时变混杂的因果推断方法主要关注估计时变二元处理对研究终点结局的平均因果效应，缺乏刻画连续暴露下边际因果剂量-反应关系的有效工具。本文提出一种可扩展的非参数贝叶斯框架，用于估计具有重复结局测量的边际纵向因果剂量-反应函数。该方法以任意固定剂量水平下的平均潜在结局为目标，并通过广义倾向评分处理时变混杂问题。所提出的方法将狄利克雷过程设定嵌入广义估计方程结构中，在捕获时间相关性的同时对连续暴露的函数形式做出最小假设。我们将所提方法应用于来自国际主要城市的月度地铁客流量与COVID-19病例数据，识别了高客流量与病例增长之间的因果关系及剂量-反应模式。