Explaining Individualized Treatment Rules: Integrating LIME and SHAP With Xgboost in Precision Medicine

Precision medicine relies on accurate and interpretable predictive models to identify patient subgroups and biomarkers that can guide individualized treatment strategies. While extreme gradient boosting (XGBoost) often achieves state-of-the-art predictive performance, its complexity can impede understanding of how input variables influence outcomes. Building upon existing XGBoost frameworks for estimating individualized treatment rule (ITR), we introduce a global permutation test within this framework to assess treatment effect heterogeneity. Additionally, we incorporate two model-agnostic explanation techniques, local interpretable model-agnostic explanations (LIME) and SHapley Additive exPlanations (SHAP), to enhance interpretability at both global and individual levels. Through simulations and analyses of real-world clinical trial datasets, we illustrate that our permutation-based pipeline can detect empirical signals of treatment effect heterogeneity, while LIME and SHAP offer exploratory insights into feature contributions and ITR.