  A/B tests are randomized experiments frequently used by companies that offer
services on the Web for assessing the impact of new features. During an
experiment, each user is randomly redirected to one of two versions of the
website, called treatments. Several response models were proposed to describe
the behavior of a user in a social network website, where the treatment
assigned to her neighbors must be taken into account. However, there is no
consensus 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 estimation
error of several models, and obtain empirical results for cases where the
response model was misspecified.
