IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2301.05580.html

Randomization Test for the Specification of Interference Structure

Author

Listed:
  • Tadao Hoshino
  • Takahide Yanagi

Abstract

This study considers testing the specification of spillover effects in causal inference. We focus on experimental settings in which the treatment assignment mechanism is known to researchers. We develop a new randomization test utilizing a hierarchical relationship between different exposures. Compared with existing approaches, our approach is essentially applicable to any null exposure specifications and produces powerful test statistics without a priori knowledge of the true interference structure. As empirical illustrations, we revisit two existing social network experiments: one on farmers' insurance adoption and the other on anti-conflict education programs.

Suggested Citation

  • Tadao Hoshino & Takahide Yanagi, 2023. "Randomization Test for the Specification of Interference Structure," Papers 2301.05580, arXiv.org, revised Dec 2023.
    • Handle: RePEc:arx:papers:2301.05580
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2301.05580
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gonzalo Vazquez-Bare, 2017. "Identification and Estimation of Spillover Effects in Randomized Experiments," Papers 1711.02745, arXiv.org, revised Jan 2022.
    2. Hong, Guanglei & Raudenbush, Stephen W., 2006. "Evaluating Kindergarten Retention Policy: A Case Study of Causal Inference for Multilevel Observational Data," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 901-910, September.
    3. Tadao Hoshino & Takahide Yanagi, 2024. "Causal Inference with Noncompliance and Unknown Interference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(548), pages 2869-2880, October.
    4. 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.
    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. David Puelz & Guillaume Basse & Avi Feller & Panos Toulis, 2022. "A graph‐theoretic approach to randomization tests of causal effects under general interference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 174-204, February.
    9. Michael P. Leung, 2022. "Causal Inference Under Approximate Neighborhood Interference," Econometrica, Econometric Society, vol. 90(1), pages 267-293, January.
    10. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, November.
    11. Luke Keele & Corrine McConnaughy & Ismail White, 2012. "Strengthening the Experimenter’s Toolbox: Statistical Estimation of Internal Validity," American Journal of Political Science, John Wiley & Sons, vol. 56(2), pages 484-499, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Julius Owusu, 2023. "Randomization Inference of Heterogeneous Treatment Effects under Network Interference," Papers 2308.00202, arXiv.org, revised Jun 2025.
    2. Tadao Hoshino, 2025. "Evaluating Policy Effects under Network Interference without Network Information: A Transfer Learning Approach," Papers 2510.14415, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Han, Kevin & Basse, Guillaume & Bojinov, Iavor, 2024. "Population interference in panel experiments," Journal of Econometrics, Elsevier, vol. 238(1).
    2. Julius Owusu, 2023. "Randomization Inference of Heterogeneous Treatment Effects under Network Interference," Papers 2308.00202, arXiv.org, revised Jun 2025.
    3. Gao, Mengsi & Ding, Peng, 2025. "Causal inference in network experiments: Regression-based analysis and design-based properties," Journal of Econometrics, Elsevier, vol. 252(PA).
    4. Nicolas Debarsy & Julie Le Gallo, 2025. "Identification of Spatial Spillovers: Do's and Don'ts," Journal of Economic Surveys, Wiley Blackwell, vol. 39(5), pages 2152-2173, December.
    5. repec:hal:journl:hal-04549691 is not listed on IDEAS
    6. Tadao Hoshino & Takahide Yanagi, 2024. "Causal Inference with Noncompliance and Unknown Interference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(548), pages 2869-2880, October.
    7. 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, revised Apr 2025.
    8. Eric Auerbach & Hongchang Guo & Max Tabord-Meehan, 2021. "The Local Approach to Causal Inference under Network Interference," Papers 2105.03810, arXiv.org, revised Mar 2025.
    9. Chabé-Ferret, Sylvain & Reynaud, Arnaud & Tène, Eva, 2021. "Water Quality, Policy Diffusion Effects and Farmers’ Behavior," TSE Working Papers 21-1229, Toulouse School of Economics (TSE).
    10. Zhaonan Qu & Ruoxuan Xiong & Jizhou Liu & Guido Imbens, 2021. "Semiparametric Estimation of Treatment Effects in Observational Studies with Heterogeneous Partial Interference," Papers 2107.12420, arXiv.org, revised Jun 2024.
    11. Sadegh Shirani & Mohsen Bayati, 2025. "On Evolution-Based Models for Experimentation Under Interference," Papers 2511.21675, arXiv.org.
    12. Zihan Zhang & Lianyan Fu & Dehui Wang, 2026. "Difference-in-Differences using Double Negative Controls and Graph Neural Networks for Unmeasured Network Confounding," Papers 2601.00603, arXiv.org.
    13. Stefan Wager & Kuang Xu, 2021. "Experimenting in Equilibrium," Management Science, INFORMS, vol. 67(11), pages 6694-6715, November.
    14. Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Apr 2024.
    15. Yechan Park & Xiaodong Yang, 2026. "Decomposition of Spillover Effects Under Misspecification: Pseudo-true Estimands and a Local-Global Extension," Papers 2602.12023, arXiv.org, revised Mar 2026.
    16. David M. Ritzwoller & Joseph P. Romano & Azeem M. Shaikh, 2024. "Randomization Inference: Theory and Applications," Papers 2406.09521, arXiv.org, revised Feb 2025.
    17. Supriya Tiwari & Pallavi Basu, 2024. "Quasi-randomization tests for network interference," Papers 2403.16673, arXiv.org, revised Sep 2025.
    18. Luofeng Liao & Christian Kroer, 2024. "Statistical Inference and A/B Testing in Fisher Markets and Paced Auctions," Papers 2406.15522, arXiv.org, revised Mar 2025.
    19. Jinglong Zhao, 2024. "Experimental Design For Causal Inference Through An Optimization Lens," Papers 2408.09607, arXiv.org, revised Aug 2024.
    20. Mariluz Mate, 2026. "What Is a Causal Effect When Firms Interact? Counterfactuals and Interdependence," Papers 2601.00279, arXiv.org.
    21. Giulio Grossi & Marco Mariani & Alessandra Mattei & Patrizia Lattarulo & Ozge Oner, 2020. "Direct and spillover effects of a new tramway line on the commercial vitality of peripheral streets. A synthetic-control approach," Papers 2004.05027, arXiv.org, revised Nov 2023.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2301.05580. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.