IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2503.19873.html
   My bibliography  Save this paper

Identification of Average Treatment Effects in Nonparametric Panel Models

Author

Listed:
  • Susan Athey
  • Guido Imbens

Abstract

This paper studies identification of average treatment effects in a panel data setting. It introduces a novel nonparametric factor model and proves identification of average treatment effects. The identification proof is based on the introduction of a consistent estimator. Underlying the proof is a result that there is a consistent estimator for the expected outcome in the absence of the treatment for each unit and time period; this result can be applied more broadly, for example in problems of decompositions of group-level differences in outcomes, such as the much-studied gender wage gap.

Suggested Citation

  • Susan Athey & Guido Imbens, 2025. "Identification of Average Treatment Effects in Nonparametric Panel Models," Papers 2503.19873, arXiv.org.
    • Handle: RePEc:arx:papers:2503.19873
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2503.19873
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Joseph Fry, 2023. "A Method of Moments Approach to Asymptotically Unbiased Synthetic Controls," Papers 2312.01209, arXiv.org, revised Mar 2024.
    2. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
    3. Stéphane Bonhomme & Elena Manresa, 2015. "Grouped Patterns of Heterogeneity in Panel Data," Econometrica, Econometric Society, vol. 83(3), pages 1147-1184, May.
    4. Dmitry Arkhangelsky & Guido Imbens, 2024. "Causal models for longitudinal and panel data: a survey," The Econometrics Journal, Royal Economic Society, vol. 27(3), pages 1-61.
    5. Francine D. Blau & Lawrence M. Kahn, 2017. "The Gender Wage Gap: Extent, Trends, and Explanations," Journal of Economic Literature, American Economic Association, vol. 55(3), pages 789-865, September.
    6. Yuan Zhang & Elizaveta Levina & Ji Zhu, 2017. "Estimating network edge probabilities by neighbourhood smoothing," Biometrika, Biometrika Trust, vol. 104(4), pages 771-783.
    7. Stéphane Bonhomme & Thibaut Lamadon & Elena Manresa, 2022. "Discretizing Unobserved Heterogeneity," Econometrica, Econometric Society, vol. 90(2), pages 625-643, March.
    8. Chen, Mingli & Fernández-Val, Iván & Weidner, Martin, 2021. "Nonlinear factor models for network and panel data," Journal of Econometrics, Elsevier, vol. 220(2), pages 296-324.
    9. Joachim Freyberger, 2018. "Non-parametric Panel Data Models with Interactive Fixed Effects," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(3), pages 1824-1851.
    10. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    11. Harold D. Chiang & Kengo Kato & Yuya Sasaki, 2023. "Inference for High-Dimensional Exchangeable Arrays," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(543), pages 1595-1605, July.
    12. Aldous, David J., 1981. "Representations for partially exchangeable arrays of random variables," Journal of Multivariate Analysis, Elsevier, vol. 11(4), pages 581-598, December.
    13. Hao Zhang & Dale L. Zimmerman, 2005. "Towards reconciling two asymptotic frameworks in spatial statistics," Biometrika, Biometrika Trust, vol. 92(4), pages 921-936, December.
    14. Bryan S. Graham, 2024. "Sparse Network Asymptotics for Logistic Regression Under Possible Misspecification," Econometrica, Econometric Society, vol. 92(6), pages 1837-1868, November.
    15. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    16. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    17. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    18. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, June.
    19. Hsiao,Cheng, 2022. "Analysis of Panel Data," Cambridge Books, Cambridge University Press, number 9781009060752, June.
    20. Lynch, James, 1984. "Canonical row-column-exchangeable arrays," Journal of Multivariate Analysis, Elsevier, vol. 15(1), pages 135-140, August.
    21. Fry, Joseph, 2024. "A method of moments approach to asymptotically unbiased Synthetic Controls," Journal of Econometrics, Elsevier, vol. 244(1).
    22. Hsiao,Cheng, 2022. "Analysis of Panel Data," Cambridge Books, Cambridge University Press, number 9781316512104, June.
    23. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291, Decembrie.
    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. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    2. Dmitry Arkhangelsky & Aleksei Samkov, 2024. "Sequential Synthetic Difference in Differences," Papers 2404.00164, arXiv.org.
    3. Ben Deaner & Chen-Wei Hsiang & Andrei Zeleneev, 2025. "Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities," Papers 2503.20769, arXiv.org, revised Mar 2025.
    4. Cheng, Tingting & Dong, Chaohua & Gao, Jiti & Linton, Oliver, 2024. "GMM estimation for high-dimensional panel data models," Journal of Econometrics, Elsevier, vol. 244(1).
    5. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
    6. Dmitry Arkhangelsky & David Hirshberg, 2023. "Large-Sample Properties of the Synthetic Control Method under Selection on Unobservables," Papers 2311.13575, arXiv.org, revised Dec 2023.
    7. Viviano, Davide & Bradic, Jelena, 2023. "Synthetic Learner: Model-free inference on treatments over time," Journal of Econometrics, Elsevier, vol. 234(2), pages 691-713.
    8. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
    9. Cummins Joseph & Miller Douglas L. & Smith Brock & Simon David, 2024. "Matching on Noise: Finite Sample Bias in the Synthetic Control Estimator," Journal of Econometric Methods, De Gruyter, vol. 13(1), pages 67-95, January.
    10. Alberto Abadie & Anish Agarwal & Raaz Dwivedi & Abhin Shah, 2024. "Doubly Robust Inference in Causal Latent Factor Models," Papers 2402.11652, arXiv.org, revised Oct 2024.
    11. Athey, Susan & Imbens, Guido W., 2022. "Design-based analysis in Difference-In-Differences settings with staggered adoption," Journal of Econometrics, Elsevier, vol. 226(1), pages 62-79.
    12. Alemán, Christian & Busch, Christopher & Ludwig, Alexander & Santaeulàlia-Llopis, Raül, 2023. "Stage-based identification of policy effects," ICIR Working Paper Series 52/23, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).
    13. Dmitry Arkhangelsky & Vasily Korovkin, 2019. "On Policy Evaluation with Aggregate Time-Series Shocks," Papers 1905.13660, arXiv.org, revised Mar 2024.
    14. Dmitry Arkhangelsky & Guido W. Imbens, 2019. "Doubly Robust Identification for Causal Panel Data Models," Papers 1909.09412, arXiv.org, revised Feb 2022.
    15. Roy Cerqueti & Raffaella Coppier & Alessandro Girardi & Marco Ventura, 2022. "The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 46-70.
    16. Lea Bottmer & Guido Imbens & Jann Spiess & Merrill Warnick, 2021. "A Design-Based Perspective on Synthetic Control Methods," Papers 2101.09398, arXiv.org, revised Jul 2023.
    17. Enzo Brox & Riccardo Di Francesco, 2024. "The Cost of Coming Out," Papers 2403.03649, arXiv.org, revised Jun 2024.
    18. Susan Athey & Mohsen Bayati & Nikolay Doudchenko & Guido Imbens & Khashayar Khosravi, 2021. "Matrix Completion Methods for Causal Panel Data Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1716-1730, October.
    19. Christian Aleman & Christopher Busch & Alexander Ludwig & Raul Santaeulalia-Llopis, 2022. "A Stage-Based Identification of Policy Effects," PIER Working Paper Archive 22-026, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    20. Pionati, Alessandro, 2025. "Latent grouped structures in panel data: a review," MPRA Paper 123954, University Library of Munich, Germany.

    More about this item

    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:2503.19873. 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.