Deciding on an appropriate intervention requires a causal model of a treatment, the outcome, and potential mediators. Causal mediation analysis lets us distinguish between direct and indirect effects of the intervention, but has mostly been studied in a static setting. In healthcare, data come in the form of complex, irregularly sampled time-series, with dynamic interdependencies between a treatment, outcomes, and mediators across time. Existing approaches to dynamic causal mediation analysis are limited to regular measurement intervals, simple parametric models, and disregard long-range mediator--outcome interactions. To address these limitations, we propose a non-parametric mediator--outcome model where the mediator is assumed to be a temporal point process that interacts with the outcome process. With this model, we estimate the direct and indirect effects of an external intervention on the outcome, showing how each of these affects the whole future trajectory. We demonstrate on semi-synthetic data that our method can accurately estimate direct and indirect effects. On real-world healthcare data, our model infers clinically meaningful direct and indirect effect trajectories for blood glucose after a surgery.

翻译：治疗方案、结果及潜在中介变量间的因果模型是决策适当干预措施的前提。因果中介分析虽能区分干预的直接与间接效应，但现有研究多局限于静态场景。在医疗领域，数据以复杂、非规则采样的时序形式呈现，治疗、结果与中介变量间存在跨时域的动态依赖关系。现有动态因果中介分析方法受限于固定测量间隔、简单参数化模型，且忽视长程中介-结果交互作用。为突破这些局限，我们提出一种非参数化中介-结果模型，将中介变量假设为与结果过程交互的时间点过程。基于该模型，我们可评估外部干预对结果的直接与间接效应，揭示二者对完整未来轨迹的影响机制。半合成数据实验表明，本方法能精确估计直接与间接效应。在真实医疗数据中，本模型推断出术后血糖具有临床意义的直接与间接效应轨迹。