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ivmte: An R Package for Implementing Marginal Treatment Effect Methods

Author

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  • Joshua Shea

    (University of Chicago - Department of Economics)

  • Alexander Torgovitsky

    (University of Chicago - Department of Economics)

Abstract

Instrumental variable (IV) strategies are widely used to estimate causal effects in economics, political science, epidemiology, and many other fields. When there is unobserved heterogeneity in causal effects, standard linear IV estimators only represent effects for complier subpopulations (Imbens and Angrist, 1994). Marginal treatment effect (MTE) methods (Heckman and Vytlacil, 1999, 2005) allow researchers to use additional assumptions to extrapolate beyond these subpopulations. In this paper, we introduce the ivmte package (Shea and Torgovitsky, 2019), which provides a flexible framework for implementing MTE methods in both point identified and partially identified settings.

Suggested Citation

  • Joshua Shea & Alexander Torgovitsky, 2020. "ivmte: An R Package for Implementing Marginal Treatment Effect Methods," Working Papers 2020-01, Becker Friedman Institute for Research In Economics.
    • Handle: RePEc:bfi:wpaper:2020-01
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    File URL: https://repec.bfi.uchicago.edu/RePEc/pdfs/BFI_WP_202001.pdf
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    References listed on IDEAS

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    1. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
    2. Pedro Carneiro & James J. Heckman & Edward J. Vytlacil, 2011. "Estimating Marginal Returns to Education," American Economic Review, American Economic Association, vol. 101(6), pages 2754-2781, October.
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    Cited by:

    1. Deniz Dutz & Ingrid Huitfeldt & Santiago Lacouture & Magne Mogstad & Alexander Torgovitsky & Winnie van Dijk, 2021. "Selection in Surveys," NBER Working Papers 29549, National Bureau of Economic Research, Inc.
      • Deniz Dutz & Ingrid Huitfeldt & Santiago Lacouture & Magne Mogstad & Alexander Torgovitsky & Winnie van Dijk, 2021. "Selection in Surveys," Discussion Papers 971, Statistics Norway, Research Department.
    2. Hoshino Tadao & Yanagi Takahide, 2022. "Estimating marginal treatment effects under unobserved group heterogeneity," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 197-216, January.

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