This website is for the distribution of "Matching" which is a
R package for
estimating causal effects by multivariate and propensity score
matching. The package provides functions for multivariate and
propensity score matching and for finding optimal balance based on
a genetic search
algorithm. A variety of univariate and multivariate tests to
determine if balance has been obtained are also provided. These tests
can also be used to determine if an experiment or quasi-experiment is
balanced on baseline covariates.
The package includes the following main user exposed functions, two
replication datasets and three demos:
finds optimal balance using multivariate matching where a genetic
search algorithm determines the weight each covariate is given.
The user can choose which function of covariate balance to
optimize from a list or provide one of her own.
performs multivariate and propensity score matching.
provides a variety of univariate and multivariate tests to determine if
This function is a wrapper for the Match()
function which separates the matching problem into subgroups
defined by a factor. This function is much faster for large
datasets than the Match()
The package is under active development so please check back for
updates. Please cite the software as follows: Sekhon, Jasjeet
S. 2011. "Multivariate and Propensity Score Matching Software with
Automated Balance Optimization: The Matching package for R."
Journal of Statistical Software. 42(7): 1-52.
Also see my "Alternative
Balance Metrics for Bias Reduction in Matching Methods for Causal
Inference" paper which critically reviews various ways to measure
balance. Cumulative probability distribution functions of
standardized statistics are advocated as balance metrics. Formal
hypothesis tests of balance should not be conducted as is common in
the matching literature because no measure of balance is a monotonic
function of bias and because balance should be optimized without
limit. However, descriptive measures of discrepancy ignore information
related to bias which is captured by probability distribution
functions of standardized statistics.
package by Luke
Keele implements a number of Rosenbaum's methods of sensitivity
analysis for matched data. One can conduct sensitivity analyses for
matched data with binary, ordinal or continuous outcomes, and for
matched data with multiple control units matched to each treated
unit. The package is designed work with the object returned by the Match()
Significant performance enhancements were provided by Nate Begeman
(Mac OS X Performance Group at Apple). And "Matching" relies on a
modified version of the Scythe
Statistical Library developed by Andrew Martin, Kevin Quinn and
Daniel Pemstein. My modified version of the library is included in
the "Matching" package.