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Instrumented difference‐in‐differences

Author

Listed:
  • Ting Ye
  • Ashkan Ertefaie
  • James Flory
  • Sean Hennessy
  • Dylan S. Small

Abstract

Unmeasured confounding is a key threat to reliable causal inference based on observational studies. Motivated from two powerful natural experiment devices, the instrumental variables and difference‐in‐differences, we propose a new method called instrumented difference‐in‐differences that explicitly leverages exogenous randomness in an exposure trend to estimate the average and conditional average treatment effect in the presence of unmeasured confounding. We develop the identification assumptions using the potential outcomes framework. We propose a Wald estimator and a class of multiply robust and efficient semiparametric estimators, with provable consistency and asymptotic normality. In addition, we extend the instrumented difference‐in‐differences to a two‐sample design to facilitate investigations of delayed treatment effect and provide a measure of weak identification. We demonstrate our results in simulated and real datasets.

Suggested Citation

  • Ting Ye & Ashkan Ertefaie & James Flory & Sean Hennessy & Dylan S. Small, 2023. "Instrumented difference‐in‐differences," Biometrics, The International Biometric Society, vol. 79(2), pages 569-581, June.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:2:p:569-581
    DOI: 10.1111/biom.13783
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    References listed on IDEAS

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    1. C de Chaisemartin & X D’HaultfŒuille, 2018. "Fuzzy Differences-in-Differences," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(2), pages 999-1028.
    2. Card, David & Krueger, Alan B, 1994. "Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania," American Economic Review, American Economic Association, vol. 84(4), pages 772-793, September.
    3. Edward H. Kennedy & Scott Lorch & Dylan S. Small, 2019. "Robust causal inference with continuous instruments using the local instrumental variable curve," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(1), pages 121-143, February.
    4. Esther Duflo, 2001. "Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment," American Economic Review, American Economic Association, vol. 91(4), pages 795-813, September.
    5. Alberto Abadie, 2005. "Semiparametric Difference-in-Differences Estimators," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(1), pages 1-19.
    6. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    7. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    8. de Chaisemartin, Clement & D'Haultfoeuille, Xavier, 2014. "Fuzzy Changes-in Changes," CAGE Online Working Paper Series 184, Competitive Advantage in the Global Economy (CAGE).
    9. 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.
    10. Rosenbaum, Paul R., 2010. "Design Sensitivity and Efficiency in Observational Studies," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 692-702.
    11. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-529, October.
    12. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    13. Elizabeth L. Ogburn & Andrea Rotnitzky & James M. Robins, 2015. "Doubly robust estimation of the local average treatment effect curve," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(2), pages 373-396, March.
    14. Vansteelandt, Stijn & VanderWeele, Tyler J. & Tchetgen, Eric J. & Robins, James M., 2008. "Multiply Robust Inference for Statistical Interactions," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1693-1704.
    15. Linbo Wang & Eric Tchetgen Tchetgen, 2018. "Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(3), pages 531-550, June.
    16. Vaart,A. W. van der, 2000. "Asymptotic Statistics," Cambridge Books, Cambridge University Press, number 9780521784504.
    17. Xu Shi & Wang Miao & Jennifer C. Nelson & Eric J. Tchetgen Tchetgen, 2020. "Multiply robust causal inference with double‐negative control adjustment for categorical unmeasured confounding," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(2), pages 521-540, April.
    18. Tan, Zhiqiang, 2006. "Regression and Weighting Methods for Causal Inference Using Instrumental Variables," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1607-1618, December.
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