  Causal mediation analysis is used to evaluate direct and indirect causal
effects of a treatment on an outcome of interest through an intermediate
variable or a mediator.It is difficult to identify the direct and indirect
causal effects because the mediator cannot be randomly assigned in many real
applications. In this article, we consider a causal model including latent
confounders between the mediator and the outcome. We present sufficient
conditions for identifying the direct and indirect effects and propose an
approach for estimating them. The performance of the proposed approach is
evaluated by simulation studies. Finally, we apply the approach to a data set
of the customer loyalty survey by a telecom company.
