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A/B test, a simple type of controlled experiment, refers to the statisticalprocedure of experimenting to compare two treatments applied to test subjects.For example, many IT companies frequently conduct A/B tests on their users whoare connected and form social networks. Often, the users' responses could berelated to the network connection. In this paper, we assume that the users, orthe test subjects of the experiments, are connected on an undirected network,and the responses of two connected users are correlated. We include thetreatment assignment, covariate features, and network connection in aconditional autoregressive model. Based on this model, we propose a designcriterion that measures the variance of the estimated treatment effect andallocate the treatment settings to the test subjects by minimizing thecriterion. Since the design criterion depends on an unknown network correlationparameter, we adopt the locally optimal design method and develop a hybridoptimization approach to obtain the optimal design. Through synthetic and realsocial network examples, we demonstrate the value of including networkdependence in designing A/B experiments and validate that the proposed locallyoptimal design is robust to the choices of parameters.

Locally Optimal Design for A/B Testing in the Presence of Covariates and Network Connection