Ivmte: An R Package for Implementing Marginal Treatment Effect Methods

19 Pages Posted: 10 Jan 2020

See all articles by Joshua Shea

Joshua Shea

University of Chicago - Department of Economics

Alexander Torgovitsky

University of Chicago

Date Written: January 7, 2020

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

Shea, Joshua and Torgovitsky, Alexander, Ivmte: An R Package for Implementing Marginal Treatment Effect Methods (January 7, 2020). University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2020-01. Available at SSRN: https://ssrn.com/abstract=3516114 or http://dx.doi.org/10.2139/ssrn.3516114

Joshua Shea

University of Chicago - Department of Economics ( email )

1101 East 58th Street
Chicago, IL 60637
United States

Alexander Torgovitsky (Contact Author)

University of Chicago ( email )

1101 East 58th Street
Chicago, IL 60637
United States

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