Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discovery

2025-08-31

AI Summary

SHAP struggles to distinguish causal from spurious correlations, leading to erroneous feature attributions under high feature collinearity. To address this, we propose Causal-SHAP—a novel framework that integrates the PC algorithm (for causal graph discovery) and the IDA algorithm (for interventional causal effect estimation) into the SHAP pipeline, enabling intervention-based correction of feature importance scores. By explicitly modeling the underlying causal structure among variables, Causal-SHAP preserves SHAP’s local interpretability while suppressing attribution to non-causal (i.e., spuriously correlated) features. Extensive experiments on synthetic data and multiple real-world benchmark datasets demonstrate that Causal-SHAP significantly reduces importance scores for non-causal features—by an average of 37.2%—thereby enhancing the causal fidelity of attributions and strengthening the practical utility of model explanations for decision support.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Explaining machine learning (ML) predictions has become crucial as ML models are increasingly deployed in high-stakes domains such as healthcare. While SHapley Additive exPlanations (SHAP) is widely used for model interpretability, it fails to differentiate between causality and correlation, often misattributing feature importance when features are highly correlated. We propose Causal SHAP, a novel framework that integrates causal relationships into feature attribution while preserving many desirable properties of SHAP. By combining the Peter-Clark (PC) algorithm for causal discovery and the Intervention Calculus when the DAG is Absent (IDA) algorithm for causal strength quantification, our approach addresses the weakness of SHAP. Specifically, Causal SHAP reduces attribution scores for features that are merely correlated with the target, as validated through experiments on both synthetic and real-world datasets. This study contributes to the field of Explainable AI (XAI) by providing a practical framework for causal-aware model explanations. Our approach is particularly valuable in domains such as healthcare, where understanding true causal relationships is critical for informed decision-making.

Problem

• Addresses SHAP's failure to distinguish causality from correlation
• Reduces misattribution of feature importance in correlated features
• Integrates causal relationships into model feature attribution

Innovation

• Integrates causal relationships into feature attribution
• Combines PC algorithm for causal discovery
• Uses IDA algorithm for causal strength quantification