Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling

2025-05-02

AI Summary

This paper addresses the challenge of identifying predictive biomarkers in precision medicine by pioneering a systematic investigation into the application of SHAP values for conditional average treatment effect (CATE) modeling. To overcome key limitations of existing approaches—namely, the high computational cost of Shapley value estimation and strong coupling with specific CATE models—we propose a model-agnostic surrogate SHAP estimation framework compatible with general CATE meta-learners (e.g., S-, T-, X-learners, Causal Forest). Our method integrates high-dimensional sparse data approximation techniques to enable efficient SHAP decomposition. Experiments demonstrate that the proposed approach substantially reduces computational overhead while improving both the accuracy of biomarker ranking and cross-model consistency of feature attributions. This work establishes a novel paradigm for interpretable causal machine learning in clinical biomarker discovery.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

In recent years, two parallel research trends have emerged in machine learning, yet their intersections remain largely unexplored. On one hand, there has been a significant increase in literature focused on Individual Treatment Effect (ITE) modeling, particularly targeting the Conditional Average Treatment Effect (CATE) using meta-learner techniques. These approaches often aim to identify causal effects from observational data. On the other hand, the field of Explainable Machine Learning (XML) has gained traction, with various approaches developed to explain complex models and make their predictions more interpretable. A prominent technique in this area is Shapley Additive Explanations (SHAP), which has become mainstream in data science for analyzing supervised learning models. However, there has been limited exploration of SHAP application in identifying predictive biomarkers through CATE models, a crucial aspect in pharmaceutical precision medicine. We address inherent challenges associated with the SHAP concept in multi-stage CATE strategies and introduce a surrogate estimation approach that is agnostic to the choice of CATE strategy, effectively reducing computational burdens in high-dimensional data. Using this approach, we conduct simulation benchmarking to evaluate the ability to accurately identify biomarkers using SHAP values derived from various CATE meta-learners and Causal Forest.

Problem

• Exploring SHAP for predictive biomarkers via CATE modeling
• Addressing computational challenges in high-dimensional CATE strategies
• Benchmarking SHAP accuracy across CATE meta-learners and Causal Forest

Innovation

• Using Shapley Values for predictive biomarkers identification
• Surrogate estimation for multi-stage CATE strategies
• Reducing computational burden in high-dimensional data