Current Status of Research on the Application of Counterfactual Causal Inference Algorithms in Medical Diagnosis and Treatment
In medical diagnosis and treatment scenarios,the complexity of variables and causal confounders,combined with machine learning methods often learning only correlations,makes it challenging to estimate causal relationships.To better address these problems using machine learning,it is essential to introduce causal inference methods.Judea Pearl's three-tiered model of causal inference highlights that counterfactual reasoning can more accurately evaluate the true impact of specific factors on outcomes,mitigating causal confounders'effects.This paper reviews the current research on applying counterfactual causal inference algorithms in various medical diagnosis and treatment scenarios.It elaborates on the research status of medical diagnosis and treatment data generation for counterfactual inference,the application research status of counterfactual inference algorithms in medical diagnosis,and the application research status of counterfactual inference algorithms in medical treatment.The research direction of the application of counterfactual causal inference algorithms in medical diagnosis and treatment is given.Based on the analysis of existing research shortcomings,future research directions are proposed.
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