A framework generates synthetic neuroimages with explicit causal control via volumetric ROI changes to produce ground-truth data for benchmarking causal AI in neuroimaging.
Dagma: Learning dags via m-matrices and a log-determinant acyclicity characterization,
2 Pith papers cite this work. Polarity classification is still indexing.
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Entropy-based sampling of graph ensembles from simulated data quantifies causal structural ambiguity and reveals artifacts in single optimized DAGs.
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Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs
Entropy-based sampling of graph ensembles from simulated data quantifies causal structural ambiguity and reveals artifacts in single optimized DAGs.