Feature Attribution in Directed Acyclic Graphs Using Edge Intervention

Computer Science > Artificial Intelligence [Submitted on 13 Jun 2026] Feature Attribution in Directed Acyclic Graphs Using Edge Intervention Qiheng Sun, Junxu Liu, Xiaokai Mao, Haocheng Xia, Jinfei Liu, Kui Ren, Haibo Hu 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 this https URL. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.15273 [cs.AI]   (or arXiv:2606.15273v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.15273 Focus to learn more Submission history From: Jinfei Liu [view email] [v1] Sat, 13 Jun 2026 12:14:11 UTC (229 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)