Certifying when decision-time information justifies adaptive experimentation
Jia Bi, Samuel Pinilla, Chenyang Zhu
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.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.