getPropensityScore

Fit propensity scores using logistic regression.

Matching algorithm based on network-flow structure.
Users are able to modify the emphasis on three different
optimization goals: two different distance measures and
the number of treated units left unmatched. The method is proposed by Pimentel
and Kelz (2019) <doi:10.1080/01621459.2020.1720693>.
The 'rrelaxiv' package, which provides an alternative solver for
the underlying network flow problems, carries an
academic license and is not available on CRAN, but
may be downloaded from Github at
<https://github.com/josherrickson/rrelaxiv/>.

Shichao Han

MultiObjMatch

Multi-Objective Matching Algorithm

Samuel D. Pimentel

getPropensityScore function

<dl><dt>data</dt>
<dd>dataframe that contains a column named "treat", the treatment
vector, and columns of covariates specified.</dd>
<dt>covs</dt>
<dd>factor of column names of covariates used for fitting a
propensity score model.</dd></dl>

Arguments

Fit propensity scores using logistic regression. — getPropensityScore

<dl>

<dt>data</dt>
<dd>dataframe that contains a column named "treat", the treatment
vector, and columns of covariates specified.</dd>


<dt>covs</dt>
<dd>factor of column names of covariates used for fitting a
propensity score model.</dd>

</dl>

getPropensityScore: Fit propensity scores using logistic regression.

Description

Usage

Value

Arguments