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Design of Randomized Experiments in Networks

21 Pages Posted: 7 Aug 2014 Last revised: 25 Jan 2015

Dylan Walker

Boston University

Lev Muchnik

Hebrew University of Jerusalem - Jerusalem School of Business Administration

Date Written: August 6, 2014


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 towards 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.

Keywords: randomized trial design, networks, networked experiments, online field experiments, digital experimentation

Suggested Citation

Walker, Dylan and Muchnik, Lev, Design of Randomized Experiments in Networks (August 6, 2014). Available at SSRN:

Dylan Walker (Contact Author)

Boston University ( email )

595 Commonwealth Avenue
Boston, MA 02215
United States


Lev Muchnik

Hebrew University of Jerusalem - Jerusalem School of Business Administration ( email )

Mount Scopus
Jerusalem, 91905

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