Feature Attribution in Directed Acyclic Graphs Using Edge Intervention
Quick Answer
DAG-SHAP introduces a novel feature attribution method for directed acyclic graphs, addressing limitations of existing Shapley value-based methods by treating feature edges as individual attribution objects.
Quick Take
This approach captures both externality and exogenous contributions effectively, validated through extensive experiments on real and synthetic datasets.
Key Points
- DAG-SHAP improves feature attribution by focusing on edges rather than nodes.
- The method captures both externality and exogenous influences of features.
- Extensive experiments confirm the effectiveness of DAG-SHAP on various datasets.
- An efficient approximation method for computing DAG-SHAP is introduced.
- Code for DAG-SHAP is available on GitHub.
Paper Resources
Source Excerpt
arXiv:2606. 15273v1 Announce Type: new 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. …
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