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.
ProceedingsofMachineLearning Research2017: 1–13
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ME 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.