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Randomization Inference of Heterogeneous Treatment Effects under Network Interference

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  • Julius Owusu

Abstract

We design randomization tests of heterogeneous treatment effects when units interact on a single connected network. Our modeling strategy allows network interference into the potential outcomes framework using the concept of exposure mapping. We consider a general class of null hypotheses -- representing different notions of constant and no treatment effects -- that are not sharp due to unknown parameters and multiple potential outcomes. To make the nulls sharp, we propose a conditional randomization method that expands on existing procedures. Our conditioning approach permits the use of functions of treatment as a conditioning variable, widening the scope of application of the randomization method of inference. We show that the resulting testing procedures based on our conditioning approach are valid. We demonstrate the testing methods using a network data set and also present the findings of an extensive Monte Carlo study.

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  • Julius Owusu, 2023. "Randomization Inference of Heterogeneous Treatment Effects under Network Interference," Papers 2308.00202, arXiv.org, revised Jan 2025.
    • Handle: RePEc:arx:papers:2308.00202
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    References listed on IDEAS

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    1. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2008. "Nonparametric Tests for Treatment Effect Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 389-405, August.
    2. Tadao Hoshino & Takahide Yanagi, 2023. "Randomization Test for the Specification of Interference Structure," Papers 2301.05580, arXiv.org, revised Dec 2023.
    3. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    4. Charles F. Manski, 2013. "Identification of treatment response with social interactions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 1-23, February.
    5. G W Basse & A Feller & P Toulis, 2019. "Randomization tests of causal effects under interference," Biometrika, Biometrika Trust, vol. 106(2), pages 487-494.
    6. 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.
    7. Susan Athey & Dean Eckles & Guido W. Imbens, 2018. "Exact p-Values for Network Interference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 230-240, January.
    8. Peng Ding & Avi Feller & Luke Miratrix, 2016. "Randomization inference for treatment effect variation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 655-671, June.
    9. 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.
    10. 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.
    11. Lan Liu & Michael G. Hudgens, 2014. "Large Sample Randomization Inference of Causal Effects in the Presence of Interference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 288-301, March.
    12. Chung, EunYi & Olivares, Mauricio, 2021. "Permutation test for heterogeneous treatment effects with a nuisance parameter," Journal of Econometrics, Elsevier, vol. 225(2), pages 148-174.
    13. Seungjin Han & Julius Owusu & Youngki Shin, 2022. "Statistical Treatment Rules under Social Interaction," Papers 2209.09077, arXiv.org, revised Nov 2022.
    14. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, November.
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    Cited by:

    1. Julius Owusu, 2024. "A Nonparametric Test of Heterogeneous Treatment Effects under Interference," Papers 2410.00733, arXiv.org.
    2. Liang Zhong, 2024. "Unconditional Randomization Tests for Interference," Papers 2409.09243, arXiv.org, revised Oct 2024.

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