Feature Attribution in Directed Acyclic Graphs Using Edge Intervention

2026-06-13

AI Summary

Traditional Shapley value methods struggle to simultaneously account for externalities among features and exogenous influences, leading to implausible explanations in complex causal structures. This work proposes DAG-SHAP, which introduces edge interventions into the Shapley attribution framework for the first time, treating edges—rather than nodes—as the fundamental units of attribution within a directed acyclic graph (DAG). This finer-grained approach enables more precise characterization of each feature’s role along causal pathways. To ensure scalability, we develop an efficient approximation algorithm and demonstrate through experiments on multiple real-world and synthetic datasets that DAG-SHAP achieves substantially improved attribution accuracy and interpretability compared to existing methods.

Abstract

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.

Problem

• Feature Attribution
• Directed Acyclic Graphs
• Causal Relationships
• Shapley Value
• Edge Intervention

Innovation

• DAG-SHAP
• edge intervention
• feature attribution
• causal graphs
• Shapley values