IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/28364.html

Double-Robust Identification for Causal Panel Data Models

Author

Listed:
  • Dmitry Arkhangelsky
  • Guido W. Imbens

Abstract

We study identification and estimation of causal effects in settings with panel data. Traditionally researchers follow model-based identification strategies relying on assumptions governing the relation between the potential outcomes and the unobserved confounders. We focus on a novel, complementary, approach to identification where assumptions are made about the relation between the treatment assignment and the unobserved confounders. We introduce different sets of assumptions that follow the two paths to identification, and develop a double robust approach. We propose estimation methods that build on these identification strategies.

Suggested Citation

  • Dmitry Arkhangelsky & Guido W. Imbens, 2021. "Double-Robust Identification for Causal Panel Data Models," NBER Working Papers 28364, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:28364
    Note: LS
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w28364.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. Nilsen, Øivind A. & Raknerud, Arvid, 2024. "Dynamics of first-time patenting firms," Research Policy, Elsevier, vol. 53(8).
    2. Corinna Ghirelli & Enkelejda Havari & Elena Meroni & Stefano Verzillo, 2023. "The long-term causal effects of winning an ERC grant," Working Papers 2313, Banco de España.
    3. Myungkou Shin, 2022. "Finitely Heterogeneous Treatment Effect in Event-study," Papers 2204.02346, arXiv.org, revised Oct 2024.
    4. Tzvetan Moev, 2025. "Correlated Synthetic Controls," Papers 2507.08918, arXiv.org.
    5. 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.
    6. Wang, Xiqian & Bian, Yong & Zhang, Qin, 2023. "The effect of cooking fuel choice on the elderly’s well-being: Evidence from two non-parametric methods," Energy Economics, Elsevier, vol. 125(C).
    7. Kirill Borusyak & Xavier Jaravel & Jann Spiess, 2024. "Revisiting Event-Study Designs: Robust and Efficient Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(6), pages 3253-3285.
    8. Jiaming Mao & Jingzhi Xu, 2020. "Ensemble Learning with Statistical and Structural Models," Papers 2006.05308, arXiv.org.
    9. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    10. 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.
    11. Simon Calmar Andersen & Louise Beuchert & Phillip Heiler & Helena Skyt Nielsen, 2023. "A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health," Papers 2308.04963, arXiv.org.
    12. Albert Chiu & Xingchen Lan & Ziyi Liu & Yiqing Xu, 2023. "Causal Panel Analysis under Parallel Trends: Lessons from a Large Reanalysis Study," Papers 2309.15983, arXiv.org, revised Jan 2026.
    13. Dmitry Arkhangelsky & Guido W. Imbens & Lihua Lei & Xiaoman Luo, 2021. "Design-Robust Two-Way-Fixed-Effects Regression For Panel Data," Papers 2107.13737, arXiv.org, revised Mar 2024.
    14. Bassier, Ihsaan, 2022. "Collective bargaining and spillovers in local labor markets," LSE Research Online Documents on Economics 118057, London School of Economics and Political Science, LSE Library.
    15. Ihsaan Bassier, 2022. "Collective bargaining and spillovers in local labor markets," CEP Discussion Papers dp1895, Centre for Economic Performance, LSE.

    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    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:nbr:nberwo:28364. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    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.