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Reducing bias in difference-in-differences models using entropy balancing

Author

Listed:
  • Matthew Cefalu
  • Brian G. Vegetabile
  • Michael Dworsky
  • Christine Eibner
  • Federico Girosi

Abstract

This paper illustrates the use of entropy balancing in difference-in-differences analyses when pre-intervention outcome trends suggest a possible violation of the parallel trends assumption. We describe a set of assumptions under which weighting to balance intervention and comparison groups on pre-intervention outcome trends leads to consistent difference-in-differences estimates even when pre-intervention outcome trends are not parallel. Simulated results verify that entropy balancing of pre-intervention outcomes trends can remove bias when the parallel trends assumption is not directly satisfied, and thus may enable researchers to use difference-in-differences designs in a wider range of observational settings than previously acknowledged.

Suggested Citation

  • Matthew Cefalu & Brian G. Vegetabile & Michael Dworsky & Christine Eibner & Federico Girosi, 2020. "Reducing bias in difference-in-differences models using entropy balancing," Papers 2011.04826, arXiv.org.
    • Handle: RePEc:arx:papers:2011.04826
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    References listed on IDEAS

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    1. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
    2. Hainmueller, Jens, 2012. "Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies," Political Analysis, Cambridge University Press, vol. 20(1), pages 25-46, January.
    3. Marcus, Jan, 2013. "The effect of unemployment on the mental health of spouses – Evidence from plant closures in Germany," Journal of Health Economics, Elsevier, vol. 32(3), pages 546-558.
    4. Peter M. Steiner & Thomas D. Cook & William R. Shadish, 2011. "On the Importance of Reliable Covariate Measurement in Selection Bias Adjustments Using Propensity Scores," Journal of Educational and Behavioral Statistics, , vol. 36(2), pages 213-236, April.
    5. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
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