Methods and Applications of Causal Reasoning in Medical Field

Xing Wu*, Jingwen Li, Quan Qian, Yue Liu, Yike Guo

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

1 Citation (Scopus)

Abstract

Causal reasoning is an important component of explainable AI and has been a key research topic across domains, especially in the medical field. O ne o f t he c ore problems is to infer the causal effect of treatment from medical data. However, when the traditional methods of dealing with effect estimations are applied to medical cases, there are obstacles such as instability, incomprehensibility, and unexplainability, which may not be able to deal with special medical data. Furthermore, there is no thorough survey of causal reasoning methods for specific medical problems. Therefore, we present a comprehensive survey of causal reasoning methods in the context of medicine, combining the advantages of both the medical field a nd causal reasoning. And take specific examples t o s how t he contribution of causal reasoning methods in disease prediction, diagnosis decision-making, treatment effect estimation, causal relationship mining, medical image analysis, and so on. This shows that causal reasoning methods have theoretical and practical significance in the medical field.

Original languageEnglish
Title of host publication2021 7th International Conference on Big Data and Information Analytics (BigDIA)
PublisherIEEE
Pages79-86
Number of pages8
ISBN (Electronic)9781665424660
ISBN (Print)9781665424677
DOIs
Publication statusPublished - Oct 2021
Event7th International Conference on Big Data and Information Analytics, BigDIA 2021 - Chongqing, China
Duration: 29 Oct 202131 Oct 2021
http://bigdia2021.cqupt.edu.cn/
https://ieeexplore.ieee.org/xpl/conhome/9619603/proceeding

Publication series

NameProceedings of International Conference on Big Data and Information Analytics (BigDIA)

Conference

Conference7th International Conference on Big Data and Information Analytics, BigDIA 2021
Country/TerritoryChina
CityChongqing
Period29/10/2131/10/21
Internet address

Scopus Subject Areas

  • Information Systems
  • Decision Sciences (miscellaneous)
  • Information Systems and Management

User-Defined Keywords

  • Causal Reasoning
  • Causality
  • Explainable AI
  • Treatment Effect Estimation

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