Takes in a school distance matrix created using information from the first-stage student match and matches schools optimally, potentially
matchSchools(
dmat,
students,
treatment,
school.id,
school.fb,
penalty,
verbose,
tol,
solver = "rlemon"
)a dataframe with two columns, one containing treated school IDs and the other containing matched control school IDs.
a distance matrix for schools, with a row for each treated school and a column for each control school.
a dataframe containing student and school covariates, with a different row for each student.
the column name of the binary treatment status indicator in
the students dataframe.
the column name of the unique school ID in the
students dataframe.
an optional list of character vectors, each containing a
subset of the column names of students. Each element of the list
should contain all the names in previous elements (producing a nested
structure).
a numeric value, treated as the cost to the objective function of excluding a treated school. If it is set lower, more schools will be excluded.
a logical value indicating whether detailed output should be printed.
a numeric tolerance value for comparing distances. It may need to be raised above the default when matching with many levels of refined balance.
Name of package used to solve underlying network flow problem, one of 'rlemon' and 'rrelaxiv'. rrelaxiv carries an academic license and is not hosted on CRAN so it must be installed separately.
Luke Keele, Penn State University, ljk20@psu.edu
Sam Pimentel, University of California, Berkeley, spi@berkeley.edu
The school.fb argument encodes a refined covariate balance
constraint: the matching algorithm optimally balances the interaction of the
variables in the first list element, then attempts to further balance the
interaction in the second element, and so on. As such variables should be
added in order of priority for balance.