Scalable Causal Structure Learning: New Opportunities in Biomedicine

• URL: http://arxiv.org/abs/2110.07785v1
• Date: Fri, 15 Oct 2021 00:45:25 GMT
• Title: Scalable Causal Structure Learning: New Opportunities in Biomedicine
• Authors: Pulakesh Upadhyaya, Kai Zhang, Can Li, Xiaoqian Jiang, Yejin Kim
• Abstract summary: We review prominent traditional, score-based and machine-learning based schemes for causal structure discovery, study some of their performance over some benchmark datasets, and discuss some of the applications to biomedicine. In the case of sufficient data, machine learning-based approaches can be scalable, can include a greater number of variables than traditional approaches, and can potentially be applied in many biomedical applications.
• Score: 13.644407210028927
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper gives a practical tutorial on popular causal structure learning models with examples of real-world data to help healthcare audiences understand and apply them. We review prominent traditional, score-based and machine-learning based schemes for causal structure discovery, study some of their performance over some benchmark datasets, and discuss some of the applications to biomedicine. In the case of sufficient data, machine learning-based approaches can be scalable, can include a greater number of variables than traditional approaches, and can potentially be applied in many biomedical applications.

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