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
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
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EML-CD recovers causal DAG structure and closed-form mechanisms via gated EML trees, matching PC/GES SHD on Sachs data while recovering 10 of 11 function families in bivariate tests and outperforming SINDy on mechanism f-MSE.
Entropy-based sampling of graph ensembles from simulated data quantifies causal structural ambiguity and reveals artifacts in single optimized DAGs.
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EML-CD: Causal Mechanism Recovery via EML Symbolic Trees in Structure Learning
EML-CD recovers causal DAG structure and closed-form mechanisms via gated EML trees, matching PC/GES SHD on Sachs data while recovering 10 of 11 function families in bivariate tests and outperforming SINDy on mechanism f-MSE.