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Front‐Door Difference‐in‐Differences Estimators

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  • Adam N. Glynn
  • Konstantin Kashin

Abstract

We develop front‐door difference‐in‐differences estimators as an extension of front‐door estimators. Under one‐sided noncompliance, an exclusion restriction, and assumptions analogous to parallel trends assumptions, this extension allows identification when the front‐door criterion does not hold. Even if the assumptions are relaxed, we show that the front‐door and front‐door difference‐in‐differences estimators may be combined to form bounds. Finally, we show that under one‐sided noncompliance, these techniques do not require the use of control units. We illustrate these points with an application to a job training study and with an application to Florida's early in‐person voting program. For the job training study, we show that these techniques can recover an experimental benchmark. For the Florida program, we find some evidence that early in‐person voting had small positive effects on turnout in 2008. This provides a counterpoint to recent claims that early voting had a negative effect on turnout in 2008.

Suggested Citation

  • Adam N. Glynn & Konstantin Kashin, 2017. "Front‐Door Difference‐in‐Differences Estimators," American Journal of Political Science, John Wiley & Sons, vol. 61(4), pages 989-1002, October.
    • Handle: RePEc:wly:amposc:v:61:y:2017:i:4:p:989-1002
    DOI: 10.1111/ajps.12311
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    Cited by:

    1. Yechan Park & Yuya Sasaki, 2024. "A Bracketing Relationship for Long-Term Policy Evaluation with Combined Experimental and Observational Data," Papers 2401.12050, arXiv.org.
    2. David Bartram, 2021. "Cross-Sectional Model-Building for Research on Subjective Well-Being: Gaining Clarity on Control Variables," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(2), pages 725-743, June.
    3. Matteo CM Casiraghi & Eugenio Cusumano & Angelos Chryssogelos, 2024. "European integration and political party logos: A ‘visual Europeanization’?," European Union Politics, , vol. 25(1), pages 86-105, March.
    4. Paul Hunermund & Elias Bareinboim, 2019. "Causal Inference and Data Fusion in Econometrics," Papers 1912.09104, arXiv.org, revised Mar 2023.
    5. Shantanu Gupta & Zachary C. Lipton & David Childers, 2020. "Estimating Treatment Effects with Observed Confounders and Mediators," Papers 2003.11991, arXiv.org, revised Jun 2021.

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