AI RESEARCH
PRCD-MAP: Learning How Much to Trust Imperfect Priors in Causal Discovery
arXiv CS.LG
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ArXi:2605.01669v1 Announce Type: cross External priors of unknown reliability create a brittle trade-off in causal discovery: blind trust amplifies errors, blind rejection wastes signal. Real priors are also \emph{heterogeneously} reliable -- physical laws are trustworthy, LLM-suggested edges are speculative -- yet existing methods either ignore priors or impose them through globally uniform trust.