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|a This chapter considers the design and analysis of networked experiments, one of the most precise tools available for studying social behavior. 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. 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, and recent approaches to interference and uncertainty in dependent data. I conclude 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.
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