An important problem in causal inference is to break down the total effect of
a treatment on an outcome into different causal pathways and to quantify the
causal effect in each pathway. For instance, in causal fairness, the total
effect of being a male employee (i.e., treatment) constitutes its direct effect
on annual income (i.e., outcome) and the indirect effect via the employee's
occupation (i.e., mediator). Causal mediation analysis (CMA) is a formal
statistical framework commonly used to reveal such underlying causal
mechanisms. One major challenge of CMA in observational studies is handling
confounders, variables that cause spurious causal relationships among
treatment, mediator, and outcome. Conventional methods assume sequential
ignorability that implies all confounders can be measured, which is often
unverifiable in practice. This work aims to circumvent the stringent sequential
ignorability assumptions and consider hidden confounders. Drawing upon proxy
strategies and recent advances in deep learning, we propose to simultaneously
uncover the latent variables that characterize hidden confounders and estimate
the causal effects. Empirical evaluations using both synthetic and
semi-synthetic datasets validate the effectiveness of the proposed method. We
further show the potentials of our approach for causal fairness analysis.

本文提出了一种基于深度学习和代理策略的因果中介分析方法，能够同时揭示隐藏混淆变量和估计因果效应，在观察性研究中有很好的应用前景，可用于处理因果公平性问题。