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ivmte

Instrumental Variables

Installation

About

The marginal treatment effect was introduced by Heckman and Vytlacil (2005) doi:10.1111/j.1468-0262.2005.00594.x to provide a choice-theoretic interpretation to instrumental variables models that maintain the monotonicity condition of Imbens and Angrist (1994) doi:10.2307/2951620. This interpretation can be used to extrapolate from the compliers to estimate treatment effects for other subpopulations. This package provides a flexible set of methods for conducting this extrapolation. It allows for parametric or nonparametric sieve estimation, and allows the user to maintain shape restrictions such as monotonicity. The package operates in the general framework developed by Mogstad, Santos and Torgovitsky (2018) doi:10.3982/ECTA15463, and accommodates either point identification or partial identification (bounds). In the partially identified case, bounds are computed using either linear programming or quadratically constrained quadratic programming. Support for four solvers is provided. Gurobi and the Gurobi R API can be obtained from . CPLEX can be obtained from . CPLEX R APIs 'Rcplex' and 'cplexAPI' are available from CRAN. MOSEK and the MOSEK R API can be obtained from . The lp_solve library is freely available from , and is included when installing its API 'lpSolveAPI', which is available from CRAN.

Key Metrics

Version 1.4.0
R ≥ 3.6.0
Published 2021-09-17 950 days ago
Needs compilation? no
License GPL-2
License GPL-3
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Maintainer

Maintainer

Joshua Shea

jkcshea@uchicago.edu

Authors

Alexander Torgovitsky

aut

Joshua Shea

aut / cre

Material

README
Reference manual
Package source

In Views

CausalInference

Vignettes

ivmte

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

ivmte archive

Depends

R ≥ 3.6.0

Imports

Formula
methods
stats
utils

Suggests

gurobi ≥ 8.1-0
slam ≥ 0.1-42
cplexAPI ≥ 1.3.3
lpSolveAPI ≥ 5.5.2.0-17
Rmosek ≥ 9.2.38
testthat ≥2.0.0
data.table ≥ 1.12.0
splines2 ≥ 0.2.8
future.apply ≥ 1.6.0
future ≥ 1.18.0
Matrix
knitr
rmarkdown
pander
AER
lsei
ggplot2
gridExtra