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.

中文翻译：

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隐藏的混杂因素的因果中介分析

因果推理中的一个重要问题是将治疗的总效果分解为不同的因果路径，并量化每个路径中的因果效果。因果中介分析（CMA）是一种正式的统计方法，用于识别和估计这些因果关系。CMA的核心是顺序可燃性假设，该隐含假设意味着对所有治疗前混杂因素进行了测量，并且它们可以捕获不同类型的混杂因素，例如，处理后混杂因素和隐藏混杂因素。这种假设通常在观察研究中无法验证，因此限制了传统方法的覆盖范围和实用性。因此，这项工作旨在通过遵循带有统一的混杂因子及其代理变量的因果图来规避严格的假设。我们的核心贡献是将深潜变量模型与代理策略相结合的算法，共同推断出一个统一的替代混杂因素，并根据观察到的变量估算CMA中的不同因果效应。使用合成和半合成数据集进行的经验评估验证了所提出方法的有效性。