wlate1

<code>data.frame</code> on which the estimation is performed.

data

Vector of baseline values for the instruments.

from

Vector of comparison values for the instruments.

Character vector of names of instruments.

A <code>lm</code> or <code>glm</code> object, or a
<code>data.frame</code>, which can be used to estimate the propensity
to take up treatment for the specified values of the
instruments.

model

Character vector of variable names for the non-excluded
variables the user wishes to condition the LATE on.

Vector of values the user wishes to condition the
<code>X</code> variables on.

eval.X

Function generates the target weight for the LATE, conditioned on a
specific value of the covariates.

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.

wlate1: Target weight for LATE

Description

Usage

Arguments

Value