Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discovery

Current explanation tools can mislead decision-makers: SHAP, a popular method for explaining AI predictions, cannot distinguish between features that actually cause outcomes versus those that merely correlate with them. This is particularly problematic in high-stakes domains like healthcare where understanding true causal relationships is essential for informed decision-making.

Correlated features receive inflated importance scores: The paper demonstrates that when features are highly correlated with each other, SHAP often misattributes importance to features that don't actually drive predictions. Causal SHAP addresses this by reducing attribution scores for features that are merely correlated with the target outcome.

A practical solution exists for more trustworthy AI explanations: The proposed Causal SHAP framework integrates causal discovery methods into the explanation process while maintaining the desirable properties of existing SHAP approaches, offering a practical tool for organizations seeking more accurate model interpretability.

Healthcare and other high-stakes domains need this approach: The paper emphasizes that domains like healthcare require understanding which features have true causal relationships to outcomes. Causal SHAP provides a framework specifically designed to meet this critical need, potentially improving the reliability of AI-assisted clinical decisions.

Validation confirms the improvement: The research validates Causal SHAP's effectiveness through experiments on both synthetic and real-world datasets, demonstrating that it successfully differentiates between causal and correlational relationships in feature attribution.