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Causal clustering: design of cluster experiments under network interference

Author

Listed:
  • Davide Viviano
  • Lihua Lei
  • Guido Imbens
  • Brian Karrer
  • Okke Schrijvers
  • Liang Shi

Abstract

This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers. We provide a framework to choose the clustering that minimizes the worst-case mean-squared error of the estimated global effect. We show that optimal clustering solves a novel penalized min-cut optimization problem computed via off-the-shelf semi-definite programming algorithms. Our analysis also characterizes simple conditions to choose between any two cluster designs, including choosing between a cluster or individual-level randomization. We illustrate the method's properties using unique network data from the universe of Facebook's users and existing data from a field experiment.

Suggested Citation

  • Davide Viviano & Lihua Lei & Guido Imbens & Brian Karrer & Okke Schrijvers & Liang Shi, 2023. "Causal clustering: design of cluster experiments under network interference," Papers 2310.14983, arXiv.org, revised Jan 2024.
    • Handle: RePEc:arx:papers:2310.14983
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    References listed on IDEAS

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    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.
    3. Alberto Abadie & Susan Athey & Guido W Imbens & Jeffrey M Wooldridge, 2023. "When Should You Adjust Standard Errors for Clustering?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(1), pages 1-35.
    4. Betsy Sinclair & Margaret McConnell & Donald P. Green, 2012. "Detecting Spillover Effects: Design and Analysis of Multilevel Experiments," American Journal of Political Science, John Wiley & Sons, vol. 56(4), pages 1055-1069, October.
    5. Vivi Alatas & Abhijit Banerjee & Rema Hanna & Benjamin A. Olken & Julia Tobias, 2012. "Targeting the Poor: Evidence from a Field Experiment in Indonesia," American Economic Review, American Economic Association, vol. 102(4), pages 1206-1240, June.
    6. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2009. "Identification of peer effects through social networks," Journal of Econometrics, Elsevier, vol. 150(1), pages 41-55, May.
    7. à ureo de Paula & Seth Richards†Shubik & Elie Tamer, 2018. "Identifying Preferences in Networks With Bounded Degree," Econometrica, Econometric Society, vol. 86(1), pages 263-288, January.
    8. Toru Kitagawa & Guanyi Wang, 2021. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," CeMMAP working papers CWP28/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Charles F. Manski, 2013. "Identification of treatment response with social interactions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 1-23, February.
    10. Jing Cai & Alain De Janvry & Elisabeth Sadoulet, 2015. "Social Networks and the Decision to Insure," American Economic Journal: Applied Economics, American Economic Association, vol. 7(2), pages 81-108, April.
    11. Keisuke Hirano & Jack R. Porter, 2009. "Asymptotics for Statistical Treatment Rules," Econometrica, Econometric Society, vol. 77(5), pages 1683-1701, September.
    12. Jeffrey M. Wooldridge, 2003. "Cluster-Sample Methods in Applied Econometrics," American Economic Review, American Economic Association, vol. 93(2), pages 133-138, May.
    13. Toru Kitagawa & Guanyi Wang, 2020. "Who Should Get Vaccinated? Individualized Allocation of Vaccines Over SIR Network," Papers 2012.04055, arXiv.org, revised Jul 2021.
    14. Tianxi Li & Lihua Lei & Sharmodeep Bhattacharyya & Koen Van den Berge & Purnamrita Sarkar & Peter J. Bickel & Elizaveta Levina, 2022. "Hierarchical Community Detection by Recursive Partitioning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(538), pages 951-968, April.
    15. Dennis Egger & Johannes Haushofer & Edward Miguel & Paul Niehaus & Michael Walker, 2022. "General Equilibrium Effects of Cash Transfers: Experimental Evidence From Kenya," Econometrica, Econometric Society, vol. 90(6), pages 2603-2643, November.
    16. Sarah Baird & J. Aislinn Bohren & Craig McIntosh & Berk Özler, 2018. "Optimal Design of Experiments in the Presence of Interference," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 844-860, December.
    17. Michael P. Leung, 2022. "Causal Inference Under Approximate Neighborhood Interference," Econometrica, Econometric Society, vol. 90(1), pages 267-293, January.
    18. 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.
    19. Michael P. Leung, 2023. "Network Cluster‐Robust Inference," Econometrica, Econometric Society, vol. 91(2), pages 641-667, March.
    20. Karthik Muralidharan & Paul Niehaus, 2017. "Experimentation at Scale," Journal of Economic Perspectives, American Economic Association, vol. 31(4), pages 103-124, Fall.
    21. Michael P. Leung, 2020. "Treatment and Spillover Effects Under Network Interference," The Review of Economics and Statistics, MIT Press, vol. 102(2), pages 368-380, May.
    22. Emily Breza & Arun G. Chandrasekhar & Tyler H. McCormick & Mengjie Pan, 2020. "Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data," American Economic Review, American Economic Association, vol. 110(8), pages 2454-2484, August.
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    Cited by:

    1. Luofeng Liao & Christian Kroer & Sergei Leonenkov & Okke Schrijvers & Liang Shi & Nicolas Stier-Moses & Congshan Zhang, 2024. "Interference Among First-Price Pacing Equilibria: A Bias and Variance Analysis," Papers 2402.07322, arXiv.org.
    2. Yuehao Bai & Azeem M. Shaikh & Max Tabord-Meehan, 2024. "A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances," Papers 2405.03910, arXiv.org.
    3. Jinglong Zhao, 2024. "Experimental Design For Causal Inference Through An Optimization Lens," Papers 2408.09607, arXiv.org, revised Aug 2024.

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