Beyond Outcome Fairness: Mechanism-Qualified Interventions for Graph Learning

Lower group disparity does not tell us which graph edges should change, or when uncertain edits should be withheld. We study these decisions through identify–qualify–expose. MD-SCM parameterizes edge propensity by task similarity, sensitive homophily, and their interaction; a joint IPW-MLE consistently estimates the declared contributions under explicit model and sampling assumptions. MIC (Mechanism Intervention Certificate) transfers compatible-parameter uncertainty into simultaneous soft-adjacency prediction-change bounds for a fixed intervention and withholds unqualified actions. MIFB matches aggregate graph statistics while separating construction-oracle actions, yielding a lower bound for statistics-restricted Lipschitz policies and an action-fidelity audit for general editors. Across four datasets, group disparity falls 15.3–38.5% versus FairSNR, with a 0.11–0.34 percentage-point accuracy cost. At equal editing budgets, disparity decreases from 1.57 to 1.01 versus random abstention, supporting selective intervention under matched evaluation. Under misspecification, target-action error falls 43.4%; MD-SCM-free audits preserve four-dataset ordering. These results support selection quality beyond model matching; certification concerns fixed-mask prediction stability, excluding subsequent edge suppression and fairness improvement. The protocol complements outcome fairness with separately testable claims about which mechanisms are edited, when edits qualify, and whether actions match their targets.