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.

翻译：从观测数据中估计直接和间接因果效应对于理解因果机制以及预测不同干预下的行为至关重要。因果中介分析是一种常用于揭示直接和间接效应的方法。深度学习在中介分析中展现出潜力，但现有方法仅假设存在同时影响处理变量、中介变量和结果变量的潜在混杂因素，未能识别不同类型的潜在混杂因素（例如，仅影响中介变量或结果变量的混杂因素）。此外，当前方法基于序列可忽略性假设，该假设在处理多种类型的潜在混杂因素时并不适用。本研究旨在规避序列可忽略性假设，并采用分段去混杂假设作为替代方案。我们提出了解耦中介分析变分自编码器（DMAVAE），该方法将潜在混杂因素的表示解耦为三种类型，以准确估计自然直接效应、自然间接效应和总效应。实验结果表明，所提方法优于现有方法，并具有较强的泛化能力。我们进一步将该方法应用于真实数据集，以展示其潜在应用价值。