Instrumental Variables: Extrapolation by Marginal Treatment Effects

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 its API 'lpSolveAPI', which is available from CRAN.

R package for Mogstad, Santos, Torgovitsky (2017).


Reference manual

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1.2.0 by Joshua Shea, a year ago

Browse source code at

Authors: Alexander Torgovitsky [aut] , Joshua Shea [aut, cre]

Documentation:   PDF Manual  

GPL-2 | GPL-3 license

Imports Formula, methods, stats, utils

Suggests gurobi, slam, cplexAPI, lpSolveAPI, testthat, data.table, splines2, Matrix, knitr, rmarkdown, pander, AER, ggplot2, gridExtra

See at CRAN