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Causal inference in the medical domain: a survey

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Abstract

Causal inference is considered a crucial topic in the medical field, as it enables the determination of causal effects for medical treatments through data analysis. However, the vast volume and complexity of medical data present significant challenges for traditional machine learning methods in accurately assessing treatment effects. Issues such as noise in the data, unstructured information, and label sparsity can lead to unstable causal identification and erroneous correlation inference. To address these challenges, we propose a systematic survey of causal inference in the medical field, which encompasses studies utilizing observational data, aimed at organizing and summarizing the key concepts, methods, and applications of causal inference. Moreover, the causal inference applications are presented across various types of medical data, including medical images and Electronic Medical Records (EMR), using specific medical cases as examples. The thorough review not only emphasizes the theoretical and practical significance of causal inference methods but also highlights potential research directions in the medical domain.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 62172267), the National Key Research and Development Program of China (2022YFB3707800), the State Key Program of National Natural Science Foundation of China (Grant No. 61936001), and the Key Research Project of Zhejiang Laboratory (No. 2021PE0AC02).

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Xing Wu, Shaoqi Peng, Jingwen Li, Weimin Li, Quan Qian & Yue Liu

  2. Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, 200444, China

    Xing Wu

  3. Medical College of Shanghai University, Shanghai University, Shanghai, 200444, China

    Jian Zhang

  4. Shanghai University Medical Imaging Diagnostic Center, Shanghai, 201103, China

    Jian Zhang

  5. Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiaotong University, Shanghai, 200233, China

    Qun Sun

  6. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, 999077, China

    Yike Guo

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  1. Xing Wu
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  8. Yue Liu
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  9. Yike Guo
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Contributions

Xing Wu: Idea, Conceptualization, Methodology, Writing-Reviewing and Editing, Funding acquisition; Shaoqi Peng: Literature Search and Analysis, Conceptualization, Methodology, Writing-Original draft preparation; Jingwen Li: Literature Search and Analysis, Methodology, Writing-Original draft preparation; Jian Zhang: Methodology, Consultation, Validation; Qun Sun: Methodology, Consultation, Validation; Weimin Li: Supervision, Validation; Quan Qian: Supervision, Validation; Yue Liu: Supervision, Validation; Yike Guo: Supervision, Validation;

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Correspondence to Xing Wu.

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Wu, X., Peng, S., Li, J. et al. Causal inference in the medical domain: a survey. Appl Intell 54, 4911–4934 (2024). https://doi.org/10.1007/s10489-024-05338-9

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