JMIR Publications

ABSTRACT

Background:

Causal structure learning refers to a problem of identifying causal structure from observational data and can have multiple applications in the field of biomedicine and healthcare.

Objective:

The paper provides a practical review and tutorial on scalable causal structure learning models with examples of real-world data to help healthcare audiences understand and apply them.

Methods:

We review traditional (combinatorial and score-based) methods for causal structure discovery as well as machine-learning-based schemes. Various traditional approaches have been studied to tackle this problem, the most important among these being the PC algorithm. This was followed by literature on score-based methods, which are computationally faster. Because of the continuous constraint on acyclicity, there are new deep learning approaches to the problem in addition to traditional and score-based methods. Such methods can also offer scalability, especially when there is a large amount of data involving multiple variables. Utilizing our own evaluation metrics and experiments on linear, nonlinear, and benchmark Sachs’s data, we aim to highlight the various advantages and disadvantages associated with these methods for the healthcare community. We also highlight recent developments in biomedicine, where causal structure learning can be applied to discover structures such as gene networks, brain connectivity networks and in cancer epidemiology.

Results:

We also compare the performance of traditional and machine learning-based algorithms for causal discovery over some benchmark datasets.

Conclusions:

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 like genetics if sufficient data is available. Furthermore, these models are more flexible than traditional models and are poised to positively affect many applications in the future.