A Bayesian nonparametric approach for causal mediation with a post-treatment confounder.

ABSTRACT: We propose a new Bayesian nonparametric method for estimating the causal effects of mediation in the presence of a post-treatment confounder. The methodology is motivated by the Rural Lifestyle Intervention Treatment Effectiveness Trial (Rural LITE) for which there is interest in estimating causal mediation effects but is complicated by the presence of a post-treatment confounder. We specify an enriched Dirichlet process mixture (EDPM) to model the joint distribution of the observed data (outcome, mediator, post-treatment confounder, treatment, and baseline confounders). For identifiability, we use the extended version of the standard sequential ignorability (SI) as introduced in Hong et al. along with a Gaussian copula model assumption. The observed data model and causal identification as

SUBMITTER: Bae W 

PROVIDER: S-EPMC11418020 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature