Causal mediation analysis (CMA) is a powerful method to dissect the total effect of a treatment into direct and mediated effects within the potential outcome framework. This is important in many scientific applications to identify the underlying mechanisms of a treatment effect. However, in many scientific applications the mediator is unobserved, but there may exist related measurements. For example, we may want to identify how changes in brain activity or structure mediate an antidepressant's effect on behavior, but we may only have access to electrophysiological or imaging brain measurements. To date, most CMA methods assume that the mediator is one-dimensional and observable, which oversimplifies such real-world scenarios. To overcome this limitation, we introduce a CMA framework that can handle complex and indirectly observed mediators based on the identifiable variational autoencoder (iVAE) architecture. We prove that the true joint distribution over observed and latent variables is identifiable with the proposed method. Additionally, our framework captures a disentangled representation of the indirectly observed mediator and yields accurate estimation of the direct and mediated effects in synthetic and semi-synthetic experiments, providing evidence of its potential utility in real-world applications.

翻译：因果中介分析（CMA）是一种在潜在结果框架内将处理的总效应分解为直接效应和中介效应的强大方法。这在许多科学应用中至关重要，用于识别处理效应的潜在机制。然而，在许多科学应用中，中介变量无法直接观测，但可能存在相关的测量指标。例如，我们可能希望识别大脑活动或结构的变化如何介导抗抑郁药物对行为的影响，但可能只能获取电生理或脑成像测量数据。迄今为止，大多数CMA方法假设中介变量是一维且可观测的，这过度简化了此类现实场景。为克服这一局限，我们提出了一种基于可识别变分自编码器（iVAE）架构的CMA框架，能够处理复杂且间接观测的中介变量。我们证明，该方法下观测变量与潜变量的真实联合分布具有可识别性。此外，我们的框架捕捉了间接观测中介变量的解耦表征，并在合成与半合成实验中实现了对直接效应和中介效应的精准估计，为现实应用中的潜在效用提供了证据。