WITHDRAWN Bayesian Mediation Analysis for Zero-Inflated Outcome with Causal Effect Decomposition

Music City Center 

Mediation analysis has gained significant attention for its ability to identify causal mechanisms underlying the relationships between a treatment and an outcome via intermediate variables. Despite the development of numerous methodologies, approaches tailored to zero-inflated outcome with explicit causal effects decomposition and interpretation remain underexplored. In this paper, we propose a flexible Bayesian mediation analysis framework capable of handling both zero-inflated count and zero-inflated continuous outcomes, while providing a clear causal effect decomposition. The framework supports a broad range of exposure and mediator distributions. This novel technique employs Bayesian models for both the mediator and the outcome, leveraging Markov Chain Monte Carlo algorithms for parameter estimation. To facilitate implementation, we developed an easy-to-use R package, mediationBayes. Simulation studies demonstrate strong performance in terms of point estimate accuracy and coverage probabilities for both overall and decomposed mediation effects. We further applied our method to microbiome data from the American Gut Project, illustrating its practical utility.

Mediation

Bayesian

Zero-inflation

Causal effect decomposition

MCMC 

ENAR