Explaining heterogeneity of individual treatment causal effects by subgroup discovery: An observational case study in antibiotics treatment of acute rhino-sinusitis
作者：
Highlights：
• A causal modelling approach for individual treatment effects identifies subgroups with robust and additive predictive value of the outcome.
• The subgroups provide insight into why individuals may respond differently to the same treatment.
• The approach is illustrated in a large real world dataset in primary care.
摘要
•A causal modelling approach for individual treatment effects identifies subgroups with robust and additive predictive value of the outcome.•The subgroups provide insight into why individuals may respond differently to the same treatment.•The approach is illustrated in a large real world dataset in primary care.
论文关键词：Individual treatment causal effects,Heterogeneity of treatment effects,Subgroup discovery,Synthetic random forests,Prediction models,Observational studies,Antibiotics treatment,Acute rhino-sinusitis
论文评审过程：Received 6 November 2020, Revised 9 March 2021, Accepted 20 April 2021, Available online 22 April 2021, Version of Record 30 April 2021.
论文官网地址：https://doi.org/10.1016/j.artmed.2021.102080
