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Tech companies (e.g., Google or Facebook) often use randomized onlineexperiments and/or A/B testing primarily based on the average treatment effectsto compare their new product with an old one. However, it is also criticallyimportant to detect qualitative treatment effects such that the new one maysignificantly outperform the existing one only under some specificcircumstances. The aim of this paper is to develop a powerful testing procedureto efficiently detect such qualitative treatment effects. We propose a scalableonline updating algorithm to implement our test procedure. It has threenovelties including adaptive randomization, sequential monitoring, and onlineupdating with guaranteed type-I error control. We also thoroughly examine thetheoretical properties of our testing procedure including the limitingdistribution of test statistics and the justification of an efficient bootstrapmethod. Extensive empirical studies are conducted to examine the finite sampleperformance of our test procedure.

An Online Sequential Test for Qualitative Treatment Effects