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Estimation of Monotone Treatment Effects in Network Experiments

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  • David Choi

Abstract

Randomized experiments on social networks pose statistical challenges, due to the possibility of interference between units. We propose new methods for finding confidence intervals on the attributable treatment effect in such settings. The methods do not require partial interference, but instead require an identifying assumption that is similar to requiring nonnegative treatment effects. Network or spatial information can be used to customize the test statistic; in principle, this can increase power without making assumptions on the data-generating process. Supplementary materials for this article are available online.

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  • David Choi, 2017. "Estimation of Monotone Treatment Effects in Network Experiments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1147-1155, July.
    • Handle: RePEc:taf:jnlasa:v:112:y:2017:i:519:p:1147-1155
    DOI: 10.1080/01621459.2016.1194845
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    References listed on IDEAS

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    1. Charles F. Manski, 2013. "Identification of treatment response with social interactions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 1-23, February.
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    8. Nickerson, David W., 2008. "Is Voting Contagious? Evidence from Two Field Experiments," American Political Science Review, Cambridge University Press, vol. 102(1), pages 49-57, February.
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    10. Robert M. Bond & Christopher J. Fariss & Jason J. Jones & Adam D. I. Kramer & Cameron Marlow & Jaime E. Settle & James H. Fowler, 2012. "A 61-million-person experiment in social influence and political mobilization," Nature, Nature, vol. 489(7415), pages 295-298, September.
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    Cited by:

    1. Clemens Possnig & Andreea Rotu{a}rescu & Kyungchul Song, 2022. "Estimating Dynamic Spillover Effects along Multiple Networks in a Linear Panel Model," Papers 2211.08995, arXiv.org.
    2. Michael P. Leung, 2021. "Rate-Optimal Cluster-Randomized Designs for Spatial Interference," Papers 2111.04219, arXiv.org, revised Sep 2022.
    3. Gonzalo Vazquez-Bare, 2017. "Identification and Estimation of Spillover Effects in Randomized Experiments," Papers 1711.02745, arXiv.org, revised Jan 2022.
    4. Michael P. Leung, 2022. "Causal Inference Under Approximate Neighborhood Interference," Econometrica, Econometric Society, vol. 90(1), pages 267-293, January.
    5. Davide Viviano & Jess Rudder, 2020. "Policy design in experiments with unknown interference," Papers 2011.08174, arXiv.org, revised May 2024.
    6. Vivek F. Farias & Andrew A. Li & Tianyi Peng & Andrew Zheng, 2022. "Markovian Interference in Experiments," Papers 2206.02371, arXiv.org, revised Jun 2022.
    7. Fredrik Savje, 2021. "Causal inference with misspecified exposure mappings: separating definitions and assumptions," Papers 2103.06471, arXiv.org, revised Mar 2023.

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