  A/B testing is an important decision-making tool in product development for
evaluating user engagement or satisfaction from a new service, feature or
product. The goal of A/B testing is to estimate the average treatment effects
(ATE) of a new change, which becomes complicated when users are interacting.
When the important assumption of A/B testing, the Stable Unit Treatment Value
Assumption (SUTVA), which states that each individual's response is affected by
their own treatment only, is not valid, the classical estimate of the ATE
usually leads to a wrong conclusion. In this paper, we propose a
cluster-adaptive network A/B testing procedure, which involves a sequential
cluster-adaptive randomization and a cluster-adjusted estimator. The
cluster-adaptive randomization is employed to minimize the cluster-level
Mahalanobis distance within the two treatment groups, so that the variance of
the estimate of the ATE can be reduced. In addition, the cluster-adjusted
estimator is used to eliminate the bias caused by network interference,
resulting in a consistent estimation for the ATE. Numerical studies suggest our
cluster-adaptive network A/B testing achieves consistent estimation with higher
efficiency. An empirical study is conducted based on a real world network to
illustrate how our method can benefit decision-making in application.
