A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigms
This work provides a structured roadmap for researchers in causal discovery, addressing gaps in existing summaries and offering guidance for future studies, though it is incremental as it synthesizes and organizes existing methods.
The paper reviews and organizes deep learning-based causal discovery methods by categorizing tasks into three variable paradigms, defining datasets and models for each, and identifying research gaps to propose future directions.
Understanding causality helps to structure interventions to achieve specific goals and enables predictions under interventions. With the growing importance of learning causal relationships, causal discovery tasks have transitioned from using traditional methods to infer potential causal structures from observational data to the field of pattern recognition involved in deep learning. The rapid accumulation of massive data promotes the emergence of causal search methods with brilliant scalability. Existing summaries of causal discovery methods mainly focus on traditional methods based on constraints, scores and FCMs, there is a lack of perfect sorting and elaboration for deep learning-based methods, also lacking some considers and exploration of causal discovery methods from the perspective of variable paradigms. Therefore, we divide the possible causal discovery tasks into three types according to the variable paradigm and give the definitions of the three tasks respectively, define and instantiate the relevant datasets for each task and the final causal model constructed at the same time, then reviews the main existing causal discovery methods for different tasks. Finally, we propose some roadmaps from different perspectives for the current research gaps in the field of causal discovery and point out future research directions.