基于边干预的图结构特征归因，更好捕捉特征间因果影响。
Feature Attribution in Directed Acyclic Graphs Using Edge Intervention
- 将特征边作为归因单元，而非单个节点
- 能同时准确衡量特征的外部性与外生影响
- 适用于有复杂因果关系的数据分析场景
基于Shapley值的特征归因方法在处理复杂特征交互和因果关系时存在局限，尤其当提供因果结构后仍难以合理解释。现有方法多采用节点中心视角，仅关注单个特征重要性，导致无法同时捕捉特征的外部性与外生影响，造成不合理解读。为此，我们提出基于边干预的新型归因方法DAG-SHAP，将每个特征边视为独立归因对象，确保外部性和外生贡献均被恰当反映。同时，我们设计了高效的近似计算方法。在真实与合成数据集上的大量实验验证了DAG-SHAP的有效性。代码已开源：https://github.com/ZJU-DIVER/DAG-SHAP。
原文摘要 · Abstract (English)
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.
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