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Fisher Information Completion Search is introduced, a source-first algorithm for additive noise models that uses one local Fisher score for both ordering and parent selection and it is proved that a node's local Fisher information equals the noise Fisher information exactly when the conditioning set contains all parent...
High-dimensional Gaussian DAG learning faces a statistical-computational gap: methods with sharp sample complexity rely on computationally expensive subset search and a supplied indegree bound, whereas polynomial-time alternatives have less favorable sample complexity. We introduce HOST, an efficient DAG learning algor...
Count data pose a challenge for score-matching-based causal discovery: derivatives are unavailable, and simply replacing them with finite differences does not generally suffice for causal discovery. We generalize SCORE's constant-curvature criterion (Rolland et al., 2022) by conditioning on the node's value, yielding t...
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