B0985
Title: General additive network effect models: A framework for the design and analysis of experiments on networks
Authors: Nathaniel Stevens - University of Waterloo (Canada) [presenting]
Abstract: As a means of continual improvement and innovation, online controlled experiments are widely used by internet and technology companies to test and evaluate product changes, and new features, and to ensure that user feedback drives decisions. This is true of companies like Twitter, LinkedIn, and Facebook, large online social networks. However, experiments on networks are complicated by the fact that the stable unit treatment value assumption (SUTVA) no longer holds. Due to the interconnectivity of users in these networks, a user's outcome may be influenced by their own treatment assignment as well as the treatment assignment of those they are socially connected. The design and analysis of the experiment must account for this. The general additive network effect (GANE) model is proposed to jointly and flexibly model treatment and network effects. Experimental design and analysis considerations are discussed in the context of the proposed model.
