Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discovery
This addresses the need for causal-aware explanations in high-stakes domains like healthcare, though it is an incremental improvement over existing SHAP methods.
The paper tackles the problem of SHAP misattributing feature importance due to correlations by proposing Causal SHAP, which integrates causal discovery to reduce attribution scores for merely correlated features, as validated on synthetic and real-world datasets.
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.