让特征归因更准：用因果发现避免相关性误导
Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discovery
- 结合因果发现与干预计算，识别特征间真实因果关系
- 在合成与真实数据上，显著降低无关相关特征的误判得分
- 适合医疗等需区分因果与相关的高风险决策场景
机器学习模型在医疗等高风险领域应用日益广泛，解释其预测变得至关重要。尽管SHapley Additive exPlanations（SHAP）被广泛用于模型可解释性，但其无法区分因果与相关性，在特征高度相关时常错误分配重要性。本文提出Causal SHAP，一种将因果关系融入特征归因的新框架，同时保留SHAP的诸多优良性质。通过结合Peter-Clark（PC）算法进行因果发现，以及干预计算当图结构缺失时（IDA）算法量化因果强度，该方法有效缓解了传统SHAP的缺陷。实验表明，Causal SHAP能显著降低与目标变量仅相关而非因果的特征的归因分数。本研究为可解释人工智能（XAI）提供了实用的因果感知解释框架，尤其适用于医疗等必须厘清真实因果关系的领域。
原文摘要 · Abstract (English)
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.
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