Two-phase adaptive experimentation framework identifies best treatment under fixed A/B budget
burkov · x · 2026-10-19
Andriy Burkov highlights an article on a practical online experimentation problem: when comparing many product variants against control across multiple outcomes (revenue, safety, engagement), standard A/B tests lose statistical power. Adaptive designs explore more efficiently but undermine valid inference. The proposed two-phase framework uses an adaptive multi-armed bandit in phase one to identify the best candidate, then runs a validation test in phase two to verify performance and estimate treatment effects within a fixed experimentation budget.
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