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On Estimating Multiple Treatment Effects with Regression

Author

Listed:
  • Paul Goldsmith-Pinkham

    (Yale University)

  • Peter Hull

    (University of Chicago)

  • Michal Kolesár

    (Princeton University)

Abstract

We study the causal interpretation of regressions on multiple dependent treatments and flexible controls. Such regressions are often used to analyze randomized control trials with multiple intervention arms, and to estimate institutional quality (e.g. teacher value-added) with observational data. We show that, unlike with a single binary treatment, these regressions do not generally estimate convex averages of causal effects—even when the treatments are conditionally randomly assigned and the controls fully address omitted variables bias. We discuss different solutions to this issue, and propose as a solution a new class of efficient estimators of weighted average treatment effects.

Suggested Citation

  • Paul Goldsmith-Pinkham & Peter Hull & Michal Kolesár, 2021. "On Estimating Multiple Treatment Effects with Regression," Working Papers 2021-41, Princeton University. Economics Department..
  • Handle: RePEc:pri:econom:2021-41
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    File URL: https://arxiv.org/pdf/2106.05024.pdf
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    References listed on IDEAS

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    11. Peter Hull, 2018. "Estimating Treatment Effects in Mover Designs," Papers 1804.06721, arXiv.org.
    12. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    13. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    14. Nicole Maestas & Kathleen J. Mullen & Alexander Strand, 2013. "Does Disability Insurance Receipt Discourage Work? Using Examiner Assignment to Estimate Causal Effects of SSDI Receipt," American Economic Review, American Economic Association, vol. 103(5), pages 1797-1829, August.
    15. Jason Abaluck & Mauricio Caceres Bravo & Peter Hull: & Amanda Starc, 2021. "Mortality Effects and Choice Across Private Health Insurance Plans," The Quarterly Journal of Economics, Oxford University Press, vol. 136(3), pages 1557-1610.
    16. Matias D. Cattaneo, 2010. "multi-valued treatment effects," The New Palgrave Dictionary of Economics,, Palgrave Macmillan.
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    18. Jeffrey R. Kling, 2006. "Incarceration Length, Employment, and Earnings," American Economic Review, American Economic Association, vol. 96(3), pages 863-876, June.
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    20. 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.
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    Cited by:

    1. Goller, Daniel & Diem, Andrea & Wolter, Stefan C., 2023. "Sitting next to a dropout: Academic success of students with more educated peers," Economics of Education Review, Elsevier, vol. 93(C).
    2. de Chaisemartin, Clément & D’Haultfœuille, Xavier, 2023. "Two-way fixed effects and differences-in-differences estimators with several treatments," Journal of Econometrics, Elsevier, vol. 236(2).
    3. Clément de Chaisemartin & Xavier D’haultfœuille, 2023. "Two-way fixed effects and differences-in-differences estimators with several treatments," Post-Print hal-04187969, HAL.
    4. Callaway, Brantly & Li, Tong, 2023. "Policy evaluation during a pandemic," Journal of Econometrics, Elsevier, vol. 236(1).
    5. Manudeep Bhuller & Henrik Sigstad, 2022. "2SLS with Multiple Treatments," Papers 2205.07836, arXiv.org, revised Oct 2023.

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    More about this item

    Keywords

    regressions; treatment effect;

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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