Exact P-Values for Network Interference

Susan Athey, Dean Eckles and Guido Imbens

Research Papers from Stanford University, Graduate School of Business

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.

JEL-codes: C14 C21 C52 (search for similar items in EconPapers)
Date: 2015-06
New Economics Papers: this item is included in nep-exp and nep-net
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Citations: View citations in EconPapers (24)

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Related works:
Journal Article: Exact p-Values for Network Interference (2018)
Working Paper: Exact P-Values for Network Interference (2015)
Working Paper: Exact P-values for Network Interference (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:ecl:stabus:3287

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