A Bayesian Causal Mediation Model for Multiple Correlated Mediators with High-Dimensional Omics Application

Thomas M. Menino Convention & Exhibition Center 

We propose a counterfactual Bayesian causal mediation model designed for multiple correlated mediators and outcomes. Built upon a rigorous counterfactual framework that addresses assumption violations caused by exposure-induced confounding, our approach first models the mediators and outcomes jointly using a multivariate Gaussian distribution. Crucially, we parameterize both the joint mean and the covariance matrix as functions of the exposure and confounders. By simultaneously modeling the full network of mediating mechanisms in this manner, the method successfully controls for inter-mediator confounding while accommodating exposure-by-mediator and exposure-by-confounder interaction effects. To alleviate the severe parameterization challenges and computational burdens inherent in this complex multivariate structure, we then adopt a Bayesian paradigm. This framework integrates a latent factorization method with a covariance regression technique to perform low-rank estimation, efficiently extracting signals from noisy, high-dimensional data. Combined with Markov chain Monte Carlo (MCMC) sampling, these two approaches yield a highly efficient pipeline capable of simultaneously handling tens to hundreds of correlated mediating features. This significantly expands the utility of causal mediation analysis in biomedical and clinical studies where sample sizes are typically small-to-moderate (e.g., fewer than a few hundred observations). Ultimately, our method decomposes the total effect into the direct effect, the joint indirect effect, and individual marginal indirect effects. Its capability to quantify the collective impact of all mediation candidates while evaluating their individual contributions offers a statistically robust and biologically realistic framework for identifying molecular targets and revealing underlying mechanisms. We rigorously validated the accuracy and robustness of our statistical inferences through extensive simulation studies, and to demonstrate its practical utility, we applied the method to real-world datasets, utilizing high-dimensional omics data as mediators to successfully uncover underlying biological mechanisms.