Shapley value-based feature attribution methods face challenges in scenarios involving complex feature interactions and causal relationships, even when a causal structure is provided. Existing methods typically adopt a node-centric view, attributing importance solely to individual features. Consequently, they often fail to simultaneously capture the externality and exogenous influence of features, leading to unreasonable interpretations. To overcome these limitations, we propose a novel feature attribution method called DAG-SHAP, which is based on edge intervention. DAG-SHAP treats each feature edge as an individual attribution object, ensuring that both externality and exogenous contributions of features are appropriately captured. Additionally, we introduce an approximation method for efficiently computing DAG-SHAP. Extensive experiments on both real and synthetic datasets validate the effectiveness of DAG-SHAP. Our code is available at https://github.com/ZJU-DIVER/DAG-SHAP.

翻译：基于Shapley值的特征归因方法在面对复杂特征交互和因果关系时，即便提供了因果结构，仍面临诸多挑战。现有方法通常采用以节点为中心的视角，仅将重要性归因于单个特征。因此，它们往往无法同时捕捉特征的外生性和外源性影响，导致解释不合理。为克服这些局限，我们提出了一种名为DAG-SHAP的新型特征归因方法，该方法基于边干预。DAG-SHAP将每条特征边视为独立的归因对象，确保能够恰当捕捉特征的外生贡献和外源性贡献。此外，我们引入了一种近似计算方法以高效求解DAG-SHAP。在真实数据集和合成数据集上的广泛实验验证了DAG-SHAP的有效性。我们的代码已开源在https://github.com/ZJU-DIVER/DAG-SHAP。