Design of randomized experiments in networks

Dylan Walker*, Lev Muchnik

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Over the last decade, the emergence of pervasive online and digitally enabled environments has created a rich source of detailed data on human behavior. Yet, the promise of big data has recently come under fire for its inability to separate correlation from causation - to derive actionable insights and yield effective policies. Fortunately, the same online platforms on which we interact on a day-to-day basis permit experimentation at large scales, ushering in a new movement toward big experiments. Randomized controlled trials are the heart of the scientific method and when designed correctly provide clean causal inferences that are robust and reproducible. However, the realization that our world is highly connected and that behavioral and economic outcomes at the individual and population level depend upon this connectivity challenges the very principles of experimental design. The proper design and analysis of experiments in networks is, therefore, critically important. In this work, we categorize and review the emerging strategies to design and analyze experiments in networks and discuss their strengths and weaknesses.

Original languageAmerican English
Pages (from-to)1940-1951
Number of pages12
JournalProceedings of the IEEE
Issue number12
StatePublished - 1 Dec 2015

Bibliographical note

Publisher Copyright:
© 1963-2012 IEEE.


  • Behavioral science
  • general
  • science
  • sociology
  • systems, man, and cybernetics


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