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

TL;DR

This paper reviews deep structural causal models, analyzing their ability to answer counterfactual questions using observational data, and discusses their guarantees, challenges, and future research directions.

Contribution

It provides a comprehensive overview of DSCMs, detailing their theoretical foundations, capabilities, limitations, and open challenges for future research and practical applications.

Findings

Analyzes the hypotheses and guarantees of DSCMs

Highlights challenges and open questions in the field

Provides guidance for selecting appropriate DSCM methods

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 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.