ivmte: An R Package for Extrapolating Instrumental Variable Estimates Away From Compliers

Joshua Shea, Alexander Torgovitsky

Research output: Contribution to journalArticlepeer-review

Abstract

Instrumental variable (IV) strategies are widely used to estimate causal effects in economics, political science, epidemiology, sociology, 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.

Original languageEnglish (US)
Pages (from-to)1-42
Number of pages42
JournalObservational Studies
Volume9
Issue number2
DOIs
StatePublished - 2023

Keywords

  • instrumental variables
  • local average treatment effect
  • marginal treatment effects
  • partial identification

ASJC Scopus subject areas

  • Modeling and Simulation
  • Numerical Analysis
  • Statistics and Probability
  • Applied Mathematics
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'ivmte: An R Package for Extrapolating Instrumental Variable Estimates Away From Compliers'. Together they form a unique fingerprint.

Cite this