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.
Towards optimal doubly robust estimation of heterogeneous causal effects.Electronic Journal of Statistics 2023; 17(2): 3008–3049
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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.