Individualized Dynamic Mediation Analysis Using Latent Factor Models

Oregon Convention Center 

Mediation analysis plays a crucial role in causal inference as it can investigate the pathways through which the treatment influences the outcome. Most existing mediation analysis assume that the 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 poses a significant challenge in 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 on the population level while capture 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 DNA methylation study demonstrate the effectiveness and superiority of our method.

Mediation analysis

Heterogeneous effects

Unmeasured confounding

Factor model

Structural equation model

Variable selection 

Biometrics Section