  Causal structure learning refers to a process of identifying causal
structures from observational data, and it can have multiple applications in
biomedicine and health care. This paper provides a practical review and
tutorial on scalable causal structure learning models with examples of
real-world data to help health care audiences understand and apply them. We
reviewed traditional (combinatorial and score-based methods) for causal
structure discovery and machine learning-based schemes. We also highlighted
recent developments in biomedicine where causal structure learning can be
applied to discover structures such as gene networks, brain connectivity
networks, and those in cancer epidemiology. We also compared the performance of
traditional and machine learning-based algorithms for causal discovery over
some benchmark data sets. Machine learning-based approaches, including deep
learning, have many advantages over traditional approaches, such as
scalability, including a greater number of variables, and potentially being
applied in a wide range of biomedical applications, such as genetics, if
sufficient data are available. Furthermore, these models are more flexible than
traditional models and are poised to positively affect many applications in the
future.
