The distribution from which <code>dtcf</code> was generated. This
distribution set is defined in the (non-exported) command
<code>gendist_covariates()</code>. It is worth noting that the
unobservable terms are alreayd integrated out.

datasets

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.

Joshua Shea

ivmte

Instrumental Variables: Extrapolation by Marginal Treatment
Effects

Alexander Torgovitsky

dtc function

A data frame with 36 rows and 10 columns.<dl class="dl-horizontal">
 <dt>x1</dt><dd>covariate 1</dd>
 <dt>x2</dt><dd>covariate 2</dd>
 <dt>z1</dt><dd>instrument 1</dd>
 <dt>z2</dt><dd>instrument 2</dd>
 <dt>latent</dt><dd>latent variable determining treatment participation</dd>
 <dt>p</dt><dd>probability of treatment uptake</dd>
 <dt>ey0</dt><dd>counterfactual outcome when not a recipient of treatment</dd>
 <dt>ey1</dt><dd>counterfactual outcome when a recipient of treatment</dd>
 <dt>f</dt><dd>density</dd>
 <dt>multiplier</dt><dd>number of observations required in data set</dd>
</dl>

Format

Covariates data set distribution — dtc

A data frame with 36 rows and 10 columns.<dl class='dl-horizontal'>
 <dt>x1</dt><dd>covariate 1</dd>
 <dt>x2</dt><dd>covariate 2</dd>
 <dt>z1</dt><dd>instrument 1</dd>
 <dt>z2</dt><dd>instrument 2</dd>
 <dt>latent</dt><dd>latent variable determining treatment participation</dd>
 <dt>p</dt><dd>probability of treatment uptake</dd>
 <dt>ey0</dt><dd>counterfactual outcome when not a recipient of treatment</dd>
 <dt>ey1</dt><dd>counterfactual outcome when a recipient of treatment</dd>
 <dt>f</dt><dd>density</dd>
 <dt>multiplier</dt><dd>number of observations required in data set</dd>
</dl>

dtc: Covariates data set distribution

Description

Usage

Arguments

Format