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

TL;DR

This review compares traditional and deep learning algorithms for scalable causal structure learning, emphasizing recent biomedical applications and highlighting the advantages of machine learning approaches in handling complex, large-scale data.

Contribution

It provides a comprehensive overview of causal structure learning methods, including recent deep learning advances, with practical examples and performance comparisons in biomedical contexts.

Findings

Deep learning approaches offer greater scalability and flexibility.

Machine learning methods outperform traditional algorithms on benchmark datasets.

Potential for broad biomedical applications with sufficient data.

Abstract

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.