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Discussion on: Instrumented difference‐in‐differences, by Ting Ye, Ashkan Ertefaie, James Flory, Sean Hennessy and Dylan S. Small

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  • Karla DiazOrdaz

Abstract

I discuss the assumptions needed for identification of average treatment effects and local average treatment effects in instrumented difference‐in‐differences (IDID), and the possible trade‐offs between assumptions of standard IV and those needed for the new proposal IDID, in one‐ and two‐sample settings. I also discuss the interpretation of the estimands identified under monotonicity. I conclude by suggesting possible extensions to the estimation method, by outlining a strategy to use data‐adaptive estimation of the nuisance parameters, based on recent developments.

Suggested Citation

  • Karla DiazOrdaz, 2023. "Discussion on: Instrumented difference‐in‐differences, by Ting Ye, Ashkan Ertefaie, James Flory, Sean Hennessy and Dylan S. Small," Biometrics, The International Biometric Society, vol. 79(2), pages 597-600, June.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:2:p:597-600
    DOI: 10.1111/biom.13785
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    References listed on IDEAS

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    1. Oliver Hines & Oliver Dukes & Karla Diaz-Ordaz & Stijn Vansteelandt, 2022. "Demystifying Statistical Learning Based on Efficient Influence Functions," The American Statistician, Taylor & Francis Journals, vol. 76(3), pages 292-304, July.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. Yifan Cui & Eric Tchetgen Tchetgen, 2021. "A Semiparametric Instrumental Variable Approach to Optimal Treatment Regimes Under Endogeneity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(533), pages 162-173, January.
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