Exact P-Values for Network Interference

41 Pages Posted: 6 Jul 2015 Last revised: 27 Mar 2023

See all articles by Susan Athey

Susan Athey

Stanford Graduate School of Business

Dean Eckles

MIT Sloan School of Management

Guido W. Imbens

Stanford Graduate School of Business

Date Written: July 2015

Abstract

We study the calculation of exact p-values for a large class of non-sharp null hypotheses about treatment effects in a setting with data from experiments involving members of a single connected network. The class includes null hypotheses that limit the effect of one unit's treatment status on another according to the distance between units; for example, the hypothesis might specify that the treatment status of immediate neighbors has no effect, or that units more than two edges away have no effect. We also consider hypotheses concerning the validity of sparsification of a network (for example based on the strength of ties) and hypotheses restricting heterogeneity in peer effects (so that, for example, only the number or fraction treated among neighboring units matters). Our general approach is to define an artificial experiment, such that the null hypothesis that was not sharp for the original experiment is sharp for the artificial experiment, and such that the randomization analysis for the artificial experiment is validated by the design of the original experiment.

Suggested Citation

Carleton Athey, Susan and Eckles, Dean and Imbens, Guido W., Exact P-Values for Network Interference (July 2015). NBER Working Paper No. w21313, Available at SSRN: https://ssrn.com/abstract=2626987

Susan Carleton Athey (Contact Author)

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Dean Eckles

MIT Sloan School of Management ( email )

Guido W. Imbens

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

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