negationCheck

data set used for estimation. The comparisons are made
only on values in the support of the data set.

data

user-defined set of functions defining the
knots associated with splines weights for the control
group. The arguments of the function should consist only of
variable names in <code>data</code>. If the knot is constant across
all observations, then the user can instead submit the value of
the weight instead of a function.

target.knots0

user-defined set of functions defining the
knots associated with splines weights for the treated
group. The arguments of the function should be variable names
in <code>data</code>. If the knot is constant across all
observations, then the user can instead submit the value of the
weight instead of a function.

target.knots1

user-defined weight function for the control
group defining the target parameter. A list of functions can be
submitted if the weighting function is in fact a spline. The
arguments of the function should be variable names in
<code>data</code>. If the weight is constant across all observations,
then the user can instead submit the value of the weight
instead of a function.

target.weight0

user-defined weight function for the treated
group defining the target parameter. A list of functions can be
submitted if the weighting function is in fact a spline. The
arguments of the function should be variable names in
<code>data</code>. If the weight is constant across all observations,
then the user can instead submit the value of the weight
instead of a function.

target.weight1

integer, default set to 20. This is the maxmimum number of
points between treated and control groups to compare and
determine whether or not the weights are indeed negations of
one another. If the data set contains fewer than <code>N</code>
unique values for a given set of variables, then all those
unique values are used for the comparison.

This function checks whether the user-declared weights for treated
and control groups are in fact negations of each other. This is
problematic for the GMM procedure when accounting for estimation
error of the target weights.

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

negationCheck: Check if custom weights are negations of each other

Description

Usage

Arguments

Value