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 in many real-world applications. Additionally, the presence of unobserved confounding variables imposes a significant challenge to inferring both causal effect and mediation effect. To address these issues, we propose an individualized dynamic mediation analysis method. Our approach can identify the significant mediators of the population level while capturing the time-varying and heterogeneous mediation effects via latent factor modeling on coefficients of structural equation models. Another advantage of our method is that we can infer individualized mediation effects in the presence of unmeasured time-varying confounders. We provide estimation consistency for our proposed causal estimand 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发挥着至关重要的作用，因为它能够探究处理变量影响结果变量的路径。大多数现有的中介效应分析假设中介效应是静态的，并且在人群中是同质的。然而，在许多实际应用中，中介效应通常会随时间变化，并表现出显著的异质性。此外，未观测到的混杂变量的存在对推断因果效应和中介效应都构成了重大挑战。为解决这些问题，我们提出了一种个体化动态中介效应分析方法。我们的方法能够在识别群体层面显著中介变量的同时，通过对结构方程模型系数的潜在因子建模，捕捉其时变和异质的中介效应。我们方法的另一个优势是，我们能够在存在未测量的时变混杂因子的情况下，推断个体化的中介效应。我们为我们提出的因果估计量提供了估计一致性，并为显著中介变量提供了选择一致性。广泛的模拟研究以及在DNA甲基化研究中的应用，证明了我们方法的有效性和优势。