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A/B tests are randomized experiments frequently used by companies that offerservices on the Web for assessing the impact of new features. During anexperiment, each user is randomly redirected to one of two versions of thewebsite, called treatments. Several response models were proposed to describethe behavior of a user in a social network website, where the treatmentassigned to her neighbors must be taken into account. However, there is noconsensus as to which model should be applied to a given dataset. In this work,we propose a new response model, derive theoretical limits for the estimationerror of several models, and obtain empirical results for cases where theresponse model was misspecified.

Modelos de Resposta para Experimentos Randomizados em Redes Sociais de Larga Escala