Certifying when decision-time information justifies adaptive experimentation

Abstract:Adaptive laboratories choose measurements during experiments, yet most methods begin after adaptation is permitted. We introduce Opportunity-aware Policy Authorization for Laboratories (\OPAL{}), a framework that decides whether adaptation should be enabled at all. \OPAL{} uses a precommitted contract to require non-trivial adaptation, controlled target risk and positive executed value after cost. We establish an impossibility boundary: source outcomes and unlabelled target covariates cannot uniformly support non-trivial authorization under unrestricted conditional outcome shift, and derive a target-calibrated recovery. Applied to an unseen 11,265-compound Cell Painting partition, the frozen gate selected 595 compounds, captured 384 positive opportunities and achieved strictly positive executed value under least-favourable completion; its 5.18\% false-activation upper bound remained below a 7.5\% limit. Among six methods, only \OPAL{} combined non-zero activation with this risk control. Locked pharmacogenomic and finite-campaign studies distinguish policy misalignment from non-certifiability, establishing authorization as a distinct layer for safe adaptive science.

Machine Learning (cs.LG)

arXiv:2607.27651 [cs.LG]

(or arXiv:2607.27651v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.27651 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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