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Doubly robust identification for causal panel data models
[Sufficient statistics for unobserved heterogeneity in structural dynamic logit models]

Author

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  • Dmitry Arkhangelsky
  • Guido W Imbens

Abstract

SummaryWe 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 observed and unobserved confounders. We focus on a different, complementary approach to identification, where assumptions are made about the connection between the treatment assignment and the unobserved confounders. Such strategies are common in cross-section settings, but rarely used with panel data. 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, 2022. "Doubly robust identification for causal panel data models [Sufficient statistics for unobserved heterogeneity in structural dynamic logit models]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 649-674.
    • Handle: RePEc:oup:emjrnl:v:25:y:2022:i:3:p:649-674.
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    File URL: http://hdl.handle.net/10.1093/ectj/utac019
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    Citations

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    Cited by:

    1. 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.
    2. 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).
    3. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Mar 2024.
    4. 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.
    5. 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.
    6. Albert Chiu & Xingchen Lan & Ziyi Liu & Yiqing Xu, 2023. "What To Do (and Not to Do) with Causal Panel Analysis under Parallel Trends: Lessons from A Large Reanalysis Study," Papers 2309.15983, arXiv.org.

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