Methods and Applications of Causal Reasoning in Medical Field

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.