  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.
