Networked Experiments

This paper considers the design and analysis of networked experiments, which are rapidly becoming one of the most precise tools available for studying social behavior. As a result of digitization, the scale, scope and complexity of networked experiments has greatly expanded in recent years, creating a need for more robust design and analysis strategies. I first review innovations in networked experimental design, assessing the implications of the experimental setting, sampling, randomization procedures and treatment assignment. I then discuss the analysis of networked experiments, with particular emphasis on modeling treatment response assumptions, inference and estimation, including discussions of recent approaches to interference and uncertainty in dependent data. I conclude by discussing important challenges facing the future of networked experimentation, in particular focusing on adaptive treatment assignment, novel randomization techniques, linking online treatments to offline responses and experimental validation of observational methods