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Guide, Not Bind: Why Defeasible Priors Fail in Augmented Lagrangian Causal Discovery
Differentiable causal discovery methods increasingly encode expert priors as forbidden-edge constraints enforced by an Augmented Lagrangian (ALM) penalty, on the assumption that a data-adaptive relaxation mechanism will discount and eventually override a rule the data consistently contradicts. We sh
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