Ivmte: An R Package for Implementing Marginal Treatment Effect Methods

38 Pages Posted: 10 Jan 2020 Last revised: 7 Sep 2021

See all articles by Joshua Shea

Joshua Shea

University of Chicago - Department of Economics

Alexander Torgovitsky

University of Chicago

Date Written: September 4, 2021

Abstract

Instrumental variable (IV) strategies are widely used to estimate causal effects in
economics, political science, epidemiology, psychology, and 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 complier subpopulations. We discuss a
flexible framework for MTE methods based on linear regression and the generalized
method of moments. We show how to implement the framework using the ivmte
package for R.

Suggested Citation

Shea, Joshua and Torgovitsky, Alexander, Ivmte: An R Package for Implementing Marginal Treatment Effect Methods (September 4, 2021). 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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