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This paper theoretically analyzes support selection in continuous DAG learning, showing that smooth acyclicity constraints alone cannot rank supports beyond feasibility and deriving selection times for NOTEARS/DAGMA, with empirical audits on 320 trajectories.
This paper introduces compatibility and incompatibility scores for evaluating collections of bivariate causal statements without relying on faithfulness, and demonstrates their applicability by analyzing causal claims from large language models.