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Life sciences · Preprint
arXiv · September 3, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint presents Resolution-Aware Experimental Design (RAED), a novel criterion for selecting experiments under partial identifiability that prioritizes minimizing the expected structural candidate set subject to false-exclusion control. The authors prove theoretical properties and demonstrate computational disagreement with standard expected information gain on three synthetic benchmarks, including a 5% false-exclusion tolerance in a mechanistic methane-oxidation model. The work is methodological and has not undergone peer review.
Preprint.
An experiment can be preferred by structural and full-latent information gain, average classification, and nuisance-marginalized informativeness while having arbitrarily poorer valid structural resolution (cross-nuisance aliasing separation proved) RAED preserves expected ordering under genuine composite Blackwell comparison In mechanistic methane-oxidation benchmark, 5% false-exclusion tolerance yields 95% joint confidence across all three structural families with finite-sample population guarantee
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This is a methodological preprint introducing a novel experimental design framework (RAED) with theoretical proofs and computational benchmarks, but without peer review or clinical application data.
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Experimental design is commonly framed as choosing the experiment expected to provide the most information. Under partial identifiability however, persistent nuisance uncertainty can make the same observation carry different structural meanings. We introduce Resolution-Aware Experimental Design (RAED), which selects an experiment by the smallest expected nonempty structural candidate set achievable subject to false-exclusion control. We prove an exact cross-nuisance aliasing separation: an experiment can be preferred by structural and full-latent information gain, average classification, and nuisance-marginalized informativeness while having arbitrarily poorer valid structural resolution. RAED nevertheless preserves the expected ordering under a genuine composite Blackwell comparison. To make this criterion operational, we develop a learned score-based implementation with finite-sample nuisance-average and positive-tail calibration, and characterize a rare-tail sample-complexity obstruction. Under constrained sensing, two subsurface-flow benchmarks exhibit genuine RAED--expected-information-gain (EIG) experiment-selection disagreements, with the clearest and largest held-out resolution differences in WCA. In a fluvial benchmark, tail protection changes the selected physical experiment and replaces hard-region false exclusions primarily with explicit ambiguity. In a mechanistic methane-oxidation benchmark, a prospectively specified 5\% false-exclusion tolerance also yields a nontrivial finite-sample population guarantee for tail-sensitive nuisance risk, with 95\% joint confidence across all three structural families.
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