  An important problem in causal inference is to break down the total effect of
treatment into different causal pathways and quantify the causal effect in each
pathway. Causal mediation analysis (CMA) is a formal statistical approach for
identifying and estimating these causal effects. Central to CMA is the
sequential ignorability assumption that implies all pre-treatment confounders
are measured and they can capture different types of confounding, e.g.,
post-treatment confounders and hidden confounders. Typically unverifiable in
observational studies, this assumption restrains both the coverage and
practicality of conventional methods. This work, therefore, aims to circumvent
the stringent assumption by following a causal graph with a unified confounder
and its proxy variables. Our core contribution is an algorithm that combines
deep latent-variable models and proxy strategy to jointly infer a unified
surrogate confounder and estimate different causal effects in CMA from observed
variables. Empirical evaluations using both synthetic and semi-synthetic
datasets validate the effectiveness of the proposed method.
