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We propose an alternative framework to existing setups for controlling falsealarms when multiple A/B tests are run over time. This setup arises in manypractical applications, e.g. when pharmaceutical companies test new treatmentoptions against control pills for different diseases, or when internetcompanies test their default webpages versus various alternatives over time.Our framework proposes to replace a sequence of A/B tests by a sequence ofbest-arm MAB instances, which can be continuously monitored by the datascientist. When interleaving the MAB tests with an an online false discoveryrate (FDR) algorithm, we can obtain the best of both worlds: low samplecomplexity and any time online FDR control. Our main contributions are: (i) topropose reasonable definitions of a null hypothesis for MAB instances; (ii) todemonstrate how one can derive an always-valid sequential p-value that allowscontinuous monitoring of each MAB test; and (iii) to show that using rejectionthresholds of online-FDR algorithms as the confidence levels for the MABalgorithms results in both sample-optimality, high power and low FDR at anypoint in time. We run extensive simulations to verify our claims, and alsoreport results on real data collected from the New Yorker Cartoon Captioncontest.

A framework for Multi-A(rmed)/B(andit) testing with online FDR control