Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges

arXiv:2405.05025v1 Announce Type: cross
Abstract: 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 …

abstract applications arxiv causal challenges counterfactual cs.lg data deep generative models generative generative models paper queries review stat.ml through type