Description Usage Format Details References Examples
Data from Lalonde (1986) and two matched datasets: One where optimal 1:1 propensity score matching was used, and one where cardinality matching was used, with the balance constraint that all standardized covariate mean differences be below 0.1.
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Three data frames:
lalonde
: 614 units (rows) and 9 variables (columns). This is the full Lalonde (1986) dataset.
lalonde.matched.ps
: 370 units (rows) and 10 variables (columns). This is the 1:1 propensity score matched dataset.
lalonde.matched.card
: 240 units (rows) and 10 variables (columns). This is the cardinality matched dataset.
All three data frames have these 9 columns:
treat
: A binary treatment variable. Equal to 1 if treated in the National Supported Work Demonstration; equal to 0 otherwise.
age
: age in years.
educ
: years of education.
black
: an indicator variable, equal to 1 only if the subject is black.
hispan
: an indicator variable, equal to 1 only if the subject is hispanic.
married
: an indicator variable, equal to 1 only if the subject is married.
nodegree
: an indicator variable, equal to 1 only if the subject does not have a degree.
re74
: earnings in 1974.
re75
: earnings in 1975.
All of the columns except treat
are covariates; in these datasets, the outcome variable is not provided.
Meanwhile, lalonde.matched.ps
and lalonde.matched.card
have one additional column, subclass
, denoting the pairs for those matched datasets.
The optimal 1:1 propensity score matched dataset was produced using the MatchIt
R
package. The propensity scores were estimated using logistic regression, where treat
was the outcome and the other variables were the covariates (within no interactions included).
The cardinality matched datset was produced using the designmatch
R
package.
LaLonde, R. J. (1986). Evaluating the econometric evaluations of training programs with experimental data. The American Economic Review, 604-620.
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