  With the extensive use of digital devices, online experimental platforms are
commonly used to conduct experiments to collect data for evaluating different
variations of products, algorithms, and interface designs, a.k.a., A/B tests.
In practice, multiple A/B testing experiments are often carried out based on a
common user population on the same platform. The same user's responses to
different experiments can be correlated to some extent due to the individual
effect of the user. In this paper, we propose a novel framework that
collaboratively analyzes the data from paired A/B tests, namely, a pair of A/B
testing experiments conducted on the same set of experimental subjects. The
proposed analysis approach for paired A/B tests can lead to more accurate
estimates than the traditional separate analysis of each experiment. We obtain
the asymptotic distribution of the proposed estimators and demonstrate that the
proposed estimators are asymptotically the best linear unbiased estimators
under certain assumptions. Moreover, the proposed analysis approach is
computationally efficient, easy to implement, and robust to different types of
responses. Both numerical simulations and numerical studies based on a real
case are used to examine the performance of the proposed method.
