Surrogate SHAP fits an XGBoost model to estimated CATEs and uses TreeSHAP to rank predictive biomarkers, with simulations favoring S-learning in RCTs and R/DR-learning in observational settings.
Subgroup identification in clinical trials: an overview of available methods and their implementations with R.Annals of translational medicine2018; 6(7)
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Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling
Surrogate SHAP fits an XGBoost model to estimated CATEs and uses TreeSHAP to rank predictive biomarkers, with simulations favoring S-learning in RCTs and R/DR-learning in observational settings.