  Estimating direct and indirect causal effects from observational data is
crucial to understanding the causal mechanisms and predicting the behaviour
under different interventions. Causal mediation analysis is a method that is
often used to reveal direct and indirect effects. Deep learning shows promise
in mediation analysis, but the current methods only assume latent confounders
that affect treatment, mediator and outcome simultaneously, and fail to
identify different types of latent confounders (e.g., confounders that only
affect the mediator or outcome). Furthermore, current methods are based on the
sequential ignorability assumption, which is not feasible for dealing with
multiple types of latent confounders. This work aims to circumvent the
sequential ignorability assumption and applies the piecemeal deconfounding
assumption as an alternative. We propose the Disentangled Mediation Analysis
Variational AutoEncoder (DMAVAE), which disentangles the representations of
latent confounders into three types to accurately estimate the natural direct
effect, natural indirect effect and total effect. Experimental results show
that the proposed method outperforms existing methods and has strong
generalisation ability. We further apply the method to a real-world dataset to
show its potential application.
