IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2605.29238.html

Graph Neural Networks for Generalized Mundlak Estimator under Network Confounding

Author

Listed:
  • Lianyan Fu
  • Rui Wang
  • Zihan Zhang

Abstract

This paper proposes a generalized Mundlak estimator based on graph neural networks (GME-GNN). The estimator is designed to mitigate bias arising from group-level heterogeneity and to accommodate within-group dependence among individuals. Traditional fixed-effects models handle group heterogeneity via group-specific intercepts, but require overly strict linear additivity and intra-group independence assumptions, and are confined to within-group comparisons. Rather than relying on intercepts, GME-GNN uses aggregated group-level balancing statistics to fully control between-group confounding, enabling valid cross-group comparisons and relaxing linearity constraints. It further employs graph neural network message-passing to adaptively learn nonlinear representations and capture intra-group interaction effects. Theoretical analysis shows that the estimator satisfies double robustness and is asymptotically normal. Simulation and empirical studies confirm its performance.

Suggested Citation

  • Lianyan Fu & Rui Wang & Zihan Zhang, 2026. "Graph Neural Networks for Generalized Mundlak Estimator under Network Confounding," Papers 2605.29238, arXiv.org.
    • Handle: RePEc:arx:papers:2605.29238
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2605.29238
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kojevnikov, Denis & Marmer, Vadim & Song, Kyungchul, 2021. "Limit theorems for network dependent random variables," Journal of Econometrics, Elsevier, vol. 222(2), pages 882-908.
    2. Laura Forastiere & Edoardo M. Airoldi & Fabrizia Mealli, 2021. "Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(534), pages 901-918, April.
    3. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    4. Sobel, Michael E., 2006. "What Do Randomized Studies of Housing Mobility Demonstrate?: Causal Inference in the Face of Interference," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1398-1407, December.
    5. Dmitry Arkhangelsky & Guido W Imbens, 2024. "Fixed Effects and the Generalized Mundlak Estimator," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(5), pages 2545-2571.
    6. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 151-184, February.
    7. Michael P. Leung, 2022. "Causal Inference Under Approximate Neighborhood Interference," Econometrica, Econometric Society, vol. 90(1), pages 267-293, January.
    8. Hudgens, Michael G. & Halloran, M. Elizabeth, 2008. "Toward Causal Inference With Interference," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 832-842, June.
    9. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. repec:hal:journl:hal-04549691 is not listed on IDEAS
    4. Ruonan Xu, 2023. "Difference-in-Differences with Interference," Papers 2306.12003, arXiv.org, revised Jan 2025.
    5. Denis Fougère & Nicolas Jacquemet, 2020. "Policy Evaluation Using Causal Inference Methods," Sciences Po Economics Publications (main) hal-03455978, HAL.
    6. Shaina J. Alexandria & Michael G. Hudgens & Allison E. Aiello, 2023. "Assessing intervention effects in a randomized trial within a social network," Biometrics, The International Biometric Society, vol. 79(2), pages 1409-1419, June.
    7. Gao, Mengsi & Ding, Peng, 2025. "Causal inference in network experiments: Regression-based analysis and design-based properties," Journal of Econometrics, Elsevier, vol. 252(PA).
    8. 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.
    9. Vazquez-Bare, Gonzalo, 2023. "Identification and estimation of spillover effects in randomized experiments," Journal of Econometrics, Elsevier, vol. 237(1).
    10. Julius Owusu & Monika Avila M'arquez, 2026. "Causal Identification under Interference: The Role of Treatment Assignment Independence," Papers 2604.22532, arXiv.org.
    11. Mäkinen, Taneli & Li, Fan & Mercatanti, Andrea & Silvestrini, Andrea, 2022. "Causal analysis of central bank holdings of corporate bonds under interference," Economic Modelling, Elsevier, vol. 113(C).
    12. Davide Viviano, 2020. "Experimental Design under Network Interference," Papers 2003.08421, arXiv.org, revised Jun 2026.
    13. Zhichao Jiang & Kosuke Imai & Anup Malani, 2023. "Statistical inference and power analysis for direct and spillover effects in two‐stage randomized experiments," Biometrics, The International Biometric Society, vol. 79(3), pages 2370-2381, September.
    14. 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.
    15. Gonzalo Vazquez-Bare, 2020. "Causal Spillover Effects Using Instrumental Variables," Papers 2003.06023, arXiv.org, revised Dec 2021.
    16. 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.
    17. Michael P. Leung, 2022. "Causal Inference Under Approximate Neighborhood Interference," Econometrica, Econometric Society, vol. 90(1), pages 267-293, January.
    18. Michael P. Leung, 2021. "Rate-Optimal Cluster-Randomized Designs for Spatial Interference," Papers 2111.04219, arXiv.org, revised Sep 2022.
    19. Silvia Noirjean & Marco Mariani & Alessandra Mattei & Fabrizia Mealli, 2025. "Exploiting Network Information to Disentangle Spillover Effects in a Field Experiment on Teens’ Museum Attendance," Journal of Educational and Behavioral Statistics, , vol. 50(4), pages 567-603, August.
    20. Yuan Yuan & Kristen M. Altenburger, 2025. "A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing," Manufacturing & Service Operations Management, INFORMS, vol. 27(6), pages 1832-1850, November.
    21. Michael P. Leung & Pantelis Loupos, 2022. "Graph Neural Networks for Causal Inference Under Network Confounding," Papers 2211.07823, arXiv.org, revised Dec 2025.

    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:2605.29238. 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: https://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.