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Causal structure learning refers to a process of identifying causalstructures from observational data, and it can have multiple applications inbiomedicine and health care. This paper provides a practical review andtutorial on scalable causal structure learning models with examples ofreal-world data to help health care audiences understand and apply them. Wereviewed traditional (combinatorial and score-based methods) for causalstructure discovery and machine learning-based schemes. We also highlightedrecent developments in biomedicine where causal structure learning can beapplied to discover structures such as gene networks, brain connectivitynetworks, and those in cancer epidemiology. We also compared the performance oftraditional and machine learning-based algorithms for causal discovery oversome benchmark data sets. Machine learning-based approaches, including deeplearning, have many advantages over traditional approaches, such asscalability, including a greater number of variables, and potentially beingapplied in a wide range of biomedical applications, such as genetics, ifsufficient data are available. Furthermore, these models are more flexible thantraditional models and are poised to positively affect many applications in thefuture.

Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine