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Exact P-Values for Network Interference

Author

Listed:
  • Athey, Susan

    (Stanford University)

  • Eckles, Dean

    (Facebook)

  • Imbens, Guido W.

    (Stanford University)

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

  • Athey, Susan & Eckles, Dean & Imbens, Guido W., 2015. "Exact P-Values for Network Interference," Research Papers 3287, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:3287
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    File URL: http://www.gsb.stanford.edu/faculty-research/working-papers/exact-p-values-network-interference
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    References listed on IDEAS

    as
    1. Eckles Dean & Karrer Brian & Ugander Johan, 2017. "Design and Analysis of Experiments in Networks: Reducing Bias from Interference," Journal of Causal Inference, De Gruyter, vol. 5(1), pages 1-23, March.
    2. Paul Goldsmith-Pinkham & Guido W. Imbens, 2013. "Social Networks and the Identification of Peer Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 253-264, July.
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    8. Scott E. Carrell & Bruce I. Sacerdote & James E. West, 2013. "From Natural Variation to Optimal Policy? The Importance of Endogenous Peer Group Formation," Econometrica, Econometric Society, vol. 81(3), pages 855-882, May.
    9. Peter M. Aronow, 2012. "A General Method for Detecting Interference Between Units in Randomized Experiments," Sociological Methods & Research, , vol. 41(1), pages 3-16, February.
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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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