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Difference-in-Differences Designs: A Practitioner's Guide

Author

Listed:
  • Andrew Baker
  • Brantly Callaway
  • Scott Cunningham
  • Andrew Goodman-Bacon
  • Pedro H. C. Sant'Anna

Abstract

Difference-in-Differences (DiD) is arguably the most popular quasi-experimental research design. Its canonical form, with two groups and two periods, is well-understood. However, empirical practices can be ad hoc when researchers go beyond that simple case. This article provides an organizing framework for discussing different types of DiD designs and their associated DiD estimators. It discusses covariates, weights, handling multiple periods, and staggered treatments. The organizational framework, however, applies to other extensions of DiD methods as well.

Suggested Citation

  • Andrew Baker & Brantly Callaway & Scott Cunningham & Andrew Goodman-Bacon & Pedro H. C. Sant'Anna, 2025. "Difference-in-Differences Designs: A Practitioner's Guide," Papers 2503.13323, arXiv.org.
  • Handle: RePEc:arx:papers:2503.13323
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    1. Victor Chernozhukov & Iván Fernández‐Val & Ye Luo, 2018. "The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages," Econometrica, Econometric Society, vol. 86(6), pages 1911-1938, November.
    2. Meyer, Bruce D & Viscusi, W Kip & Durbin, David L, 1995. "Workers' Compensation and Injury Duration: Evidence from a Natural Experiment," American Economic Review, American Economic Association, vol. 85(3), pages 322-340, June.
    3. Fan, Yanqin & Yu, Zhengfei, 2012. "Partial identification of distributional and quantile treatment effects in difference-in-differences models," Economics Letters, Elsevier, vol. 115(3), pages 511-515.
    4. Borgschulte, Mark & Vogler, Jacob, 2020. "Did the ACA Medicaid expansion save lives?," Journal of Health Economics, Elsevier, vol. 72(C).
    5. Janet Currie & Hannes Schwandt, 2016. "Mortality Inequality: The Good News from a County-Level Approach," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 29-52, Spring.
    6. Black, Bernard & Hollingsworth, Alex & Nunes, Letícia & Simon, Kosali, 2022. "Simulated power analyses for observational studies: An application to the Affordable Care Act Medicaid expansion," Journal of Public Economics, Elsevier, vol. 213(C).
    7. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iv'an Fern'andez-Val, 2017. "Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," Papers 1712.04802, arXiv.org, revised Oct 2023.
    8. Guido Imbens & Nathan Kallus & Xiaojie Mao, 2021. "Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions: From Two-Way Fixed Effects to Factor Models," Papers 2108.03849, arXiv.org.
    9. 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.
    10. Sasaki, Yuya & Ura, Takuya, 2022. "Estimation And Inference For Moments Of Ratios With Robustness Against Large Trimming Bias," Econometric Theory, Cambridge University Press, vol. 38(1), pages 66-112, February.
    11. Neng-Chieh Chang, 2020. "Double/debiased machine learning for difference-in-differences models," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 177-191.
    12. Paul Goldsmith-Pinkham & Peter Hull & Michal Kolesár, 2022. "Contamination Bias in Linear Regressions," Working Papers 2022-15, Princeton University. Economics Department..
    13. Alberto Abadie & Susan Athey & Guido W. Imbens & Jeffrey M. Wooldridge, 2020. "Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis," Econometrica, Econometric Society, vol. 88(1), pages 265-296, January.
    14. Paul Goldsmith-Pinkham & Peter Hull & Michal Kolesár, 2024. "Contamination Bias in Linear Regressions," American Economic Review, American Economic Association, vol. 114(12), pages 4015-4051, December.
    15. Manasi Deshpande & Yue Li, 2019. "Who Is Screened Out? Application Costs and the Targeting of Disability Programs," American Economic Journal: Economic Policy, American Economic Association, vol. 11(4), pages 213-248, November.
    16. James J. Heckman & Jeffrey Smith & Nancy Clements, 1997. "Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 487-535.
    17. Ricardo Mora & Iliana Reggio, 2019. "Alternative diff-in-diffs estimators with several pretreatment periods," Econometric Reviews, Taylor & Francis Journals, vol. 38(5), pages 465-486, May.
    18. Ashesh Rambachan & Jonathan Roth, 2023. "A More Credible Approach to Parallel Trends," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(5), pages 2555-2591.
    19. Malani, Anup & Reif, Julian, 2015. "Interpreting pre-trends as anticipation: Impact on estimated treatment effects from tort reform," Journal of Public Economics, Elsevier, vol. 124(C), pages 1-17.
    20. Carolina Caetano & Brantly Callaway, 2024. "Difference-in-Differences when Parallel Trends Holds Conditional on Covariates," Papers 2406.15288, arXiv.org, revised Sep 2024.
    21. Janet Currie & Henrik Kleven & Esmée Zwiers, 2020. "Technology and Big Data Are Changing Economics: Mining Text to Track Methods," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 42-48, May.
    22. Joshua D. Angrist, 1998. "Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants," Econometrica, Econometric Society, vol. 66(2), pages 249-288, March.
    23. Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012. "Inverse Probability Tilting for Moment Condition Models with Missing Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(3), pages 1053-1079.
    24. Alexandre Poirier & Tymon S{l}oczy'nski, 2024. "Quantifying the Internal Validity of Weighted Estimands," Papers 2404.14603, arXiv.org, revised Mar 2025.
    25. Itzik Fadlon & Torben Heien Nielsen, 2021. "Family Labor Supply Responses to Severe Health Shocks: Evidence from Danish Administrative Records," American Economic Journal: Applied Economics, American Economic Association, vol. 13(3), pages 1-30, July.
    26. Stephen G. Donald & Kevin Lang, 2007. "Inference with Difference-in-Differences and Other Panel Data," The Review of Economics and Statistics, MIT Press, vol. 89(2), pages 221-233, May.
    27. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    28. Charles F. Manski & John V. Pepper, 2018. "How Do Right-to-Carry Laws Affect Crime Rates? Coping with Ambiguity Using Bounded-Variation Assumptions," The Review of Economics and Statistics, MIT Press, vol. 100(2), pages 232-244, May.
    29. Stéphane Bonhomme & Ulrich Sauder, 2011. "Recovering Distributions in Difference-in-Differences Models: A Comparison of Selective and Comprehensive Schooling," The Review of Economics and Statistics, MIT Press, vol. 93(2), pages 479-494, May.
    30. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
    31. Finkelstein, Amy & McKnight, Robin, 2008. "What did Medicare do? The initial impact of Medicare on mortality and out of pocket medical spending," Journal of Public Economics, Elsevier, vol. 92(7), pages 1644-1668, July.
    32. Jaap H. Abbring & Gerard J. van den Berg, 2003. "The Nonparametric Identification of Treatment Effects in Duration Models," Econometrica, Econometric Society, vol. 71(5), pages 1491-1517, September.
    33. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, December.
    34. Holger Dette & Martin Schumann, 2024. "Testing for Equivalence of Pre-Trends in Difference-in-Differences Estimation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(4), pages 1289-1301, October.
    35. Yukun Ma & Pedro H. C. Sant'Anna & Yuya Sasaki & Takuya Ura, 2023. "Doubly Robust Estimators with Weak Overlap," Papers 2304.08974, arXiv.org, revised Apr 2023.
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