A supervised graph neural network, fed with 114 statistical and information-theoretic edge features, predicts causal graphs and enforces acyclicity via post-hoc probabilistic inference.
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From Observations to Causations: A GNN-based Probabilistic Prediction Framework for Causal Discovery
A supervised graph neural network, fed with 114 statistical and information-theoretic edge features, predicts causal graphs and enforces acyclicity via post-hoc probabilistic inference.