matchMulti (version 1.1.12.1)

matchMulti-package: matchMulti Package

Description

matchMulti provides and easy to use set of functions to do matching with multilevel data. It is designed for use with grouped data such as students in schools, where the user wishes to match a set of treated groups to control groups to make the two groups more comparable.

Author

Maintainer: Sam Pimentel

Authors:

• Luke Keele

• Luke Miratrix

Other contributors:

• Paul Rosenbaum [contributor]

Details

This package will match treated groups to control groups, but allows for trimming of both units and groups to increase balance. There are also functions for assessing balance after matching, estimating treatment effects and performing sensitivity analysis for hidden confounders.

See Also

See also matchMulti, matchMultisens, balanceMulti, matchMultioutcome, rematchSchools

Examples

if (FALSE) {
# Load Catholic school data
data(catholic_schools)

student.cov <- c('minority','female','ses','mathach')

# Check balance student balance before matching
balanceTable(catholic_schools[c(student.cov,'sector')],  treatment = 'sector')

#Match schools but not students within schools
match.simple <- matchMulti(catholic_schools, treatment = 'sector',
school.id = 'school', match.students = FALSE)

#Check balance after matching - this checks both student and school balance
balanceMulti(match.simple, student.cov = student.cov)

#Estimate treatment effect
output <- matchMultioutcome(match.simple, out.name = "mathach",
schl_id_name = "school",  treat.name = "sector")

# Perform sensitivity analysis using Rosenbaum bound -- increase Gamma to increase effect of
# possible hidden confounder 
         
matchMultisens(match.simple, out.name = "mathach",
          schl_id_name = "school", 
          treat.name = "sector", Gamma=1.3)
          
# Now match both schools and students within schools          
match.out <- matchMulti(catholic_schools, treatment = 'sector',
school.id = 'school', match.students = TRUE, student.vars = student.cov)

# Check balance again
bal.tab <- balanceMulti(match.out, student.cov = student.cov)

# Now match with fine balance constraints on whether the school is large 
# or has a high percentage of minority students
match.fb <- matchMulti(catholic_schools, treatment = 'sector', school.id = 'school', 
match.students = TRUE, student.vars = student.cov, 
school.fb = list(c('size_large'),c('size_large','minority_mean_large')))

# Estimate treatment effects
matchMultioutcome(match.fb, out.name = "mathach", schl_id_name = "school",  treat.name = "sector")

#Check Balance
balanceMulti(match.fb, student.cov = student.cov)
}