We present a general causal generative modelling framework for accurate estimation of high fidelity image counterfactuals with deep structural causal models. Estimation of interventional and counterfactual queries for high-dimensional structured variables, such as images, remains a challenging task. We leverage ideas from causal mediation analysis and advances in generative modelling to design new deep causal mechanisms for structured variables in causal models. Our experiments demonstrate that our proposed mechanisms are capable of accurate abduction and estimation of direct, indirect and total effects as measured by axiomatic soundness of counterfactuals.

翻译：我们提出了一种通用的因果生成建模框架，用于通过深度结构因果模型精确估计高保真图像反事实。对于高维结构化变量（如图像）的干预查询和反事实查询的估计仍是一项具有挑战性的任务。我们借鉴因果中介分析的思想和生成建模的最新进展，为因果模型中的结构化变量设计了新的深度因果机制。实验表明，我们提出的机制能够实现精确的外推，并估计直接效应、间接效应和总效应，这通过反事实的公理正确性得以验证。