Mediation analysis plays a crucial role in causal inference as it can investigate the pathways through which treatment influences outcome. Most existing mediation analysis assumes that mediation effects are static and homogeneous within populations. However, mediation effects usually change over time and exhibit significant heterogeneity among individuals in many real-world applications. Additionally, the mediation mechanism can be complicated and involves non-sparse, making mediator selection particularly challenging. To address these issues, we propose an individualized dynamic mediation analysis method for mediator selection. Our approach can identify the significant mediators at the population level while capturing the time-varying and heterogeneous mediation effects at the individual level via varying-coefficient structural equation models. Another advantage of our method is that we allow the presence of unmeasured time-varying confounders that induce the heterogeneous mediation effects. We provide asymptotic results for the proposed estimator and selection consistency for significant mediators. Extensive simulation studies and an application to a DNA methylation study demonstrate the effectiveness and advantages of our method.

翻译：中介效应分析在因果推断中扮演着关键角色，它能够探究处理变量影响结果变量的作用路径。现有的大多数中介分析方法假定中介效应在总体中是静态且同质的。然而，在许多实际应用中，中介效应通常会随时间变化，并在个体间表现出显著的异质性。此外，中介机制可能非常复杂且涉及非稀疏性，这使得中介变量的选择尤为困难。为解决这些问题，我们提出了一种用于中介变量选择的个体化动态中介效应分析方法。我们的方法能够在总体水平上识别出显著的中介变量，同时通过变系数结构方程模型在个体水平上捕捉其时变和异质的中介效应。我们方法的另一个优势是允许存在未观测的时变混杂因子，这些混杂因子可能导致异质的中介效应。我们为所提出的估计量提供了渐近理论结果，并证明了其对显著中介变量的选择一致性。大量的模拟研究及一项DNA甲基化研究的应用实例验证了我们方法的有效性和优势。