This paper provides a comprehensive review of deep structural causal models
(DSCMs), particularly focusing on their ability to answer counterfactual
queries using observational data within known causal structures. It delves into
the characteristics of DSCMs by analyzing the hypotheses, guarantees, and
applications inherent to the underlying deep learning components and structural
causal models, fostering a finer understanding of their capabilities and
limitations in addressing different counterfactual queries. Furthermore, it
highlights the challenges and open questions in the field of deep structural
causal modeling. It sets the stages for researchers to identify future work
directions and for practitioners to get an overview in order to find out the
most appropriate methods for their needs.

本文全面综述了深度结构因果模型 (DSCMs) 的特点以及其在使用观测数据回答反事实查询方面的能力，分析了其关于深度学习组件和结构因果模型的假设、保证和应用，深入理解其在不同反事实查询情境下的能力和限制，同时强调了深度结构因果建模领域面临的挑战和未解之问，为研究者确定未来工作方向和实践者选择最合适的方法提供了概述。