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This paper establishes identifiability conditions for causal DAGs with mixed ordinal and exponential family nodes under linear parametric models, proving that edge orientations can be determined from the joint distribution alone with sufficient categories and support points.
Proposes DAG-SHAP, a novel feature attribution method based on edge intervention for directed acyclic graphs, addressing limitations of existing Shapley value methods in capturing feature interactions and causal relationships.