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This work addresses the attribution problem with an observable evidence standard that separates opportunity to act, executed-plan change, operational consequence, equivalence, uncertainty, and operating scope from heterogeneous planning additions, and applies it to heterogeneous planning additions.
Value sorting should remain a permanent benchmark, and ML should be deployed only after severity learnability and calibration have been audited and the model clears that gate under leakage-controlled rolling-origin evaluation.
CriticalSCM-Bench v1 is presented, a controlled synthetic benchmark with causal ground truth, paired factual/counterfactual rollouts, and an explicit net-value objective that identifies regimes in which adaptive ranking adds value and those in which simpler structural policies remain preferable.
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