PRCD-MAP: Learning How Much to Trust Imperfect Priors in Causal Discovery

External causal knowledge can improve structure learning, but incorrect or unevenly reliable priors can distort the learned graph. We propose PRCD-MAP, a framework that learns bounded trust parameters to modulate sparsity and ridge penalties in structural vector autoregression. Grouped temperatures calibrate trust across prior-probability bins; an optional contextual MLP assigns edge-specific trust. Trust updates use an empirical-Bayes-inspired agreement-and-curvature surrogate. Our analysis gives conditional estimation and local perturbation bounds, separating regularization robustness from causal identification and optimization guarantees. Experiments cover synthetic temporal graphs, CausalTime, electricity consumption, nonlinear and cross-sectional models, prior corruption, scalability, and component ablations. Cached data-driven priors improve AUROC over the no-prior backbone by 0.067 and 0.089 on AQI and Medical. In a controlled structured-error suite, learned trust reaches AUROC 0.850 versus 0.759 for a no-prior ablation, and distinguishes correct from incorrect prior assertions with AUROC 0.796. Fixed trust reaches 0.863 in that suite, and grouped temperatures lead a matched architecture comparison. Adaptive trust improves recovery in some tested regimes, but its benefits depend on sample size, prior reliability, and the optimization procedure.