Overview and Practical Recommendations on Using Shapley Values for Identifying Predictive Biomarkers via CATE...

Area of Science:

• Machine Learning
• Causal Inference
• Explainable AI

Background:

• Individual Treatment Effect (ITE) modeling, specifically Conditional Average Treatment Effect (CATE) using meta-learners, is advancing causal inference from observational data.
• Explainable Machine Learning (XML), notably Shapley Additive Explanations (SHAP), enhances model interpretability in data science.
• The intersection of SHAP and CATE for predictive biomarker identification in precision medicine is underexplored.

Purpose of the Study:

• To address challenges in applying SHAP to multi-stage CATE strategies.
• To introduce a surrogate estimation approach for SHAP in CATE models.
• To enable efficient identification of predictive biomarkers using SHAP values in high-dimensional settings.

Main Methods:

• Developed a surrogate estimation approach for SHAP values in CATE modeling.
• The approach is agnostic to the specific CATE meta-learner strategy.
• Employed simulation benchmarking to evaluate biomarker identification accuracy.

Main Results:

• The proposed surrogate estimation method effectively reduces computational burden in high-dimensional data.
• Simulation results demonstrate accurate identification of biomarkers using SHAP values derived from various CATE meta-learners and Causal Forest.
• The approach facilitates the application of SHAP for biomarker discovery within CATE frameworks.

Conclusions:

• The surrogate SHAP estimation approach offers a computationally efficient and effective method for biomarker identification in CATE models.
• This work bridges the gap between explainable AI and causal inference for precision medicine.
• The findings support the use of SHAP for discovering predictive biomarkers in complex treatment effect modeling.