ivmte

Instrumental Variables: Extrapolation by Marginal Treatment Effects

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 linear programming. Support for three linear programming solvers is provided. Gurobi and the Gurobi R API can be obtained from <http://www.gurobi.com/index>. CPLEX can be obtained from <https://www.ibm.com/analytics/cplex-optimizer>. CPLEX R APIs 'Rcplex' and 'cplexAPI' are available from CRAN. The lp_solve library is freely available from <http://lpsolve.sourceforge.net/5.5/>, and is included when installing either of its R APIs, 'lpSolve' or 'lpSolveAPI', which are available from CRAN.

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Description file content

Package
ivmte
Title
Instrumental Variables: Extrapolation by Marginal Treatment Effects
Version
1.0.1
Maintainer
Joshua Shea
Description
The marginal treatment effect was introduced by Heckman and Vytlacil (2005) to provide a choice-theoretic interpretation to instrumental variables models that maintain the monotonicity condition of Imbens and Angrist (1994) . 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) , and accommodates either point identification or partial identification (bounds). In the partially identified case, bounds are computed using linear programming. Support for three linear programming 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. The lp_solve library is freely available from , and is included when installing either of its R APIs, 'lpSolve' or 'lpSolveAPI', which are available from CRAN.
Depends
R (>= 3.4.0)
Imports
polynom (>= 1.3-9), Formula, methods, stats, utils
Suggests
gurobi (>= 7.5-1), slam (>= 0.1-42), Rcplex (>= 0.3.3), cplexAPI (>= 1.3.3), lpSolve (>= 5.6.13), lpSolveAPI (>= 3.4.4), testthat (>= 2.0.0), data.table (>= 1.11.2), splines2 (>= 0.2.8), Matrix
License
GPL-2 | GPL-3
Encoding
UTF-8
LazyData
true
RoxygenNote
6.1.1
NeedsCompilation
no
Packaged
2019-05-15 22:38:27 UTC; joshua
Author
Alexander Torgovitsky [aut], Joshua Shea [aut, cre]
Repository
CRAN
Date/Publication
2019-05-16 11:10:11 UTC

install.packages('ivmte')

1.0.1

a month ago

Joshua Shea

GPL-2 | GPL-3

Depends on

R (>= 3.4.0)

Imports

polynom (>= 1.3-9), Formula, methods, stats, utils

Suggests

gurobi (>= 7.5-1), slam (>= 0.1-42), Rcplex (>= 0.3.3), cplexAPI (>= 1.3.3), lpSolve (>= 5.6.13), lpSolveAPI (>= 3.4.4), testthat (>= 2.0.0), data.table (>= 1.11.2), splines2 (>= 0.2.8), Matrix

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