Coupling Noisy Pairwise Knowledge to the DAG Posterior for Causal Discovery

Excerpt

arXiv:2610.04559v1 Announce Type: cross Abstract: External causal reports can improve structure learning from limited observations, but their reliability varies across sources and variable pairs. We introduce HB-NoisyKG, a Bayesian framework that combines observational data with repeated causal reports from sources such as large language models. Each report is a noisy observation of a direct pair state implied by one DAG. A feature-conditioned Beta prior pools information about pair reliability,