Causal mediation analysis has been of great interest, due to its strong ability for disentangling the effects of a treatment on an outcome via a variety of paths through either the mediator(s) or the treatment. Recently, mediation analysis on multiple mediators is attracting much attention, where the relationship between the multiple mediators play an important role. In this paper, we review and extend the concept of multiple mediators uncausally related, which depicts the phenomenon that the multiple mediators are related given the baseline covariates but their correlation structure cannot be causally ordered or identified. We further provide a copula-based approach jointly modelling the mediators. A doubly robust approach is also proposed to tackle model misspecification. Theoretical properties and simulation studies are also presented, with the theoretical standard error derived based on the sandwich formula. We finally apply the proposed method on a genetic psychiatric study dataset.