Abstract

This chapter considers the design and analysis of networked experiments. As a result of digitization, the scale, scope, and complexity of networked experiments have expanded significantly in recent years, creating a need for more robust design and analysis strategies. This chapter first reviews innovations in networked experimental design, assessing the implications of the experimental setting, sampling, randomization procedures, and treatment assignment. Then the analysis of networked experiments is discussed, with particular emphasis on modeling treatment response assumptions, inference, and estimation, and recent approaches to interference and uncertainty in dependent data. The chapter concludes by discussing important challenges facing the future of networked experimentation, focusing on adaptive treatment assignment, novel randomization techniques, linking online treatments to offline responses, and experimental validation of observational methods. I hope this framework can help guide future work toward a cumulative research tradition in networked experimentation.