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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 several null hypotheses representing different notions of homogeneous treatment effects. However, these hypotheses are not sharp due to nuisance parameters and multiple potential outcomes. To address the issue of multiple potential outcomes, we propose a conditional randomization method that expands on existing procedures. Our conditioning approach permits the use of treatment assignment as a conditioning variable, widening the range of application of the randomization method of inference. In addition, we propose techniques that overcome the nuisance parameter issue. We show that our resulting testing methods based on the conditioning procedure and the strategies for handling nuisance parameters are asymptotically valid. We demonstrate the testing methods using a network data set and also present the findings of a 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 2024.
    • 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.
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    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.
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    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.
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    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, January.
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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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