Network A/B Testing: From Sampling to Estimation

A/B testing, also known as bucket testing, split testing, or controlled experiment, is a standard way to evaluate user engagement or satisfaction from a new service, feature, or product. It is widely used in online websites, including social network sites such as Facebook, LinkedIn, and Twitter to make data-driven decisions. The goal of A/B testing is to es-timate the treatment effects of a new change, which becomes intricate when users are interacting, i.e., the treatment ef-fects of a user may spill over to other users via underlying social connections. When conducting these online controlled experiments, it is a common practice to make the Stable Unit Treatment Value Assumption (SUTVA) that each in-dividual’s response is affected by their own treatment only. Though this assumption simplifies the estimation of treat