PRCD-MAP: Learning How Much to Trust Imperfect Priors in Causal Discovery
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 heterogeneously reliable -- physical laws are trustworthy, LLM-suggested edges are speculative -- yet existing methods either ignore priors or impose them through globally uniform trust. We propose PRCD-MAP, a soft prior-consumption layer that assigns per-edge trust to an imperfect prior and uses it to modulate a prior-aware $\ell_1$ and prior-weighted $\ell_2$ regularizer in a MAP objective. Trust is calibrated by empirical Bayes on a Laplace-approximated marginal likelihood and propagated along the prior graph by an MLP,...
| Authors | Xihang Shan, Da Zhou |
|---|---|
| Primary category | stat.ML |
| Published | 3 May 2026 |
| Last updated at source | 7 May 2026 |
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