Always-On Experimentation
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 stop...
| Authors | Ricardo J. Sandoval, David Arbour, Avi Feller et al. |
|---|---|
| Primary category | stat.ME |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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