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Model-assisted design of experiments in the presence of network-correlated outcomes

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  • Guillaume W Basse
  • Edoardo M Airoldi

Abstract

SUMMARYIn this paper we consider how to assign treatment in a randomized experiment in which the correlation among the outcomes is informed by a network available pre-intervention. Working within the potential outcome causal framework, we develop a class of models that posit such a correlation structure among the outcomes. We use these models to develop restricted randomization strategies for allocating treatment optimally, by minimizing the mean squared error of the estimated average treatment effect. Analytical decompositions of the mean squared error, due both to the model and to the randomization distribution, provide insights into aspects of the optimal designs. In particular, the analysis suggests new notions of balance based on specific network quantities, in addition to classical covariate balance. The resulting balanced optimal restricted randomization strategies are still design-unbiased when the model used to derive them does not hold. We illustrate how the proposed treatment allocation strategies improve on allocations that ignore the network structure.

Suggested Citation

  • Guillaume W Basse & Edoardo M Airoldi, 2018. "Model-assisted design of experiments in the presence of network-correlated outcomes," Biometrika, Biometrika Trust, vol. 105(4), pages 849-858.
    • Handle: RePEc:oup:biomet:v:105:y:2018:i:4:p:849-858.
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    File URL: http://hdl.handle.net/10.1093/biomet/asy036
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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.
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    4. Huseyin Cavusoglu & Tuan Q. Phan & Hasan Cavusoglu & Edoardo M. Airoldi, 2016. "Assessing the Impact of Granular Privacy Controls on Content Sharing and Disclosure on Facebook," Information Systems Research, INFORMS, vol. 27(4), pages 848-879, December.
    5. Cosma Rohilla Shalizi & Andrew C. Thomas, 2011. "Homophily and Contagion Are Generically Confounded in Observational Social Network Studies," Sociological Methods & Research, , vol. 40(2), pages 211-239, May.
    6. Rosenbaum, Paul R., 2007. "Interference Between Units in Randomized Experiments," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 191-200, March.
    7. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
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    Cited by:

    1. Davide Viviano, 2020. "Experimental Design under Network Interference," Papers 2003.08421, arXiv.org, revised Jul 2022.
    2. Ariel Boyarsky & Hongseok Namkoong & Jean Pouget-Abadie, 2023. "Modeling Interference Using Experiment Roll-out," Papers 2305.10728, arXiv.org, revised Aug 2023.
    3. Heather Mathews & Alexander Volfovsky, 2023. "Community informed experimental design," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(4), pages 1141-1166, October.
    4. Braun, Martin & Verdier, Valentin, 2023. "Estimation of spillover effects with matched data or longitudinal network data," Journal of Econometrics, Elsevier, vol. 233(2), pages 689-714.
    5. Vivek F. Farias & Andrew A. Li & Tianyi Peng & Andrew Zheng, 2022. "Markovian Interference in Experiments," Papers 2206.02371, arXiv.org, revised Jun 2022.
    6. Anish Agarwal & Sarah H. Cen & Devavrat Shah & Christina Lee Yu, 2022. "Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference," Papers 2210.11355, arXiv.org, revised Oct 2023.

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