Always-On Experimentation

Abstract:Generative AI has dramatically accelerated the rate at which new treatments---from novel pharmaceuticals to online marketing campaigns---can be conceived and deployed. As a result, modern experimentation platforms often run continuously, with treatments added as they are ready and removed when they underperform. We formalize this "Always-On" experimental setting, in which treatments can be dynamically generated, added to, and removed from a running experiment, and study the statistical problem of deciding whether to accept or reject each treatment while controlling for the false discovery rate. We develop sequential tests that achieve time-uniform Type-I error control under arbitrary stopping times and "predictable" treatment schedules. Our approach builds on the testing-by-betting framework: we construct test supermartingales for testing the average treatment effect of each treatment, and show that the construction of these test supermartingales is growth-rate optimal in an almost-sure sense.

Methodology (stat.ME); Artificial Intelligence (cs.AI)

arXiv:2609.38695 [stat.ME]

(or arXiv:2609.38695v1 [stat.ME] for this version)

https://doi.org/10.48550/arXiv.2609.38695 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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