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A/B testing is an important decision-making tool in product development forevaluating user engagement or satisfaction from a new service, feature orproduct. 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 ValueAssumption (SUTVA), which states that each individual's response is affected bytheir own treatment only, is not valid, the classical estimate of the ATEusually leads to a wrong conclusion. In this paper, we propose acluster-adaptive network A/B testing procedure, which involves a sequentialcluster-adaptive randomization and a cluster-adjusted estimator. Thecluster-adaptive randomization is employed to minimize the cluster-levelMahalanobis distance within the two treatment groups, so that the variance ofthe estimate of the ATE can be reduced. In addition, the cluster-adjustedestimator is used to eliminate the bias caused by network interference,resulting in a consistent estimation for the ATE. Numerical studies suggest ourcluster-adaptive network A/B testing achieves consistent estimation with higherefficiency. An empirical study is conducted based on a real world network toillustrate how our method can benefit decision-making in application.

Cluster-Adaptive Network A/B Testing: From Randomization to Estimation