  We propose a new Bayesian non-parametric (BNP) method for estimating the
causal effects of mediation in 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
confounders, treatment, and baseline confounders). The proposed BNP model
allows more confounder-based clusters than clusters for the outcome and
mediator. For identifiability, we use the extended version of the standard
sequential ignorability as introduced in \citet{hong2022posttreatment}. The
observed data model and causal identification assumptions enable us to estimate
and identify the causal effects of mediation, $i.e.$, the natural direct
effects (NDE), and indirect effects (NIE). We conduct simulation studies to
assess the performance of our proposed method. Furthermore, we apply this
approach to evaluate the causal mediation effect in the Rural LITE trial,
demonstrating its practical utility in real-world scenarios. \keywords{Causal
inference; Enriched Dirichlet process mixture model.}
