method = "quick" performs generalized full
matching, which is a form of subclassification wherein all units, both
treatment and control (i.e., the "full" sample), are assigned to a subclass
and receive at least one match. It uses an algorithm that is extremely fast
compared to optimal full matching, which is why it is labeled as "quick", at the
expense of true optimality. The method is described in Sävje, Higgins, & Sekhon (2021). The method relies on and is a wrapper
Advantages of generalized full matching include that the matching order is not required to be specified, units do not need to be discarded, and it is less likely that extreme within-subclass distances will be large, unlike with standard subclassification. The primary output of generalized full matching is a set of matching weights that can be applied to the matched sample; in this way, generalized full matching can be seen as a robust alternative to propensity score weighting, robust in the sense that the propensity score model does not need to be correct to estimate the treatment effect without bias.
This page details the allowable arguments with
method = "quick".
matchit() for an explanation of what each argument means in a general
context and how it can be specified.
Below is how
matchit() is used for generalized full matching:
a two-sided formula object containing the treatment and covariates to be used in creating the distance measure used in the matching. This formula will be supplied to the functions that estimate the distance measure.
a data frame containing the variables named in
If not found in
data, the variables will be sought in the
set here to
the distance measure to be used. See
for allowable options. Cannot be supplied as a matrix.
distance is specified as a method of estimating
propensity scores, an additional argument controlling the link function used
in estimating the distance measure. See
distance for allowable
options with each option.
a named list containing additional arguments
supplied to the function that estimates the distance measure as determined
by the argument to
a string containing the desired estimand. Allowable options
"ATE". The estimand controls
how the weights are computed; see the Computing Weights section at
matchit() for details.
for which variables exact matching should take place.
for which variables Mahalanobis distance matching should take
distance corresponds to a propensity score (e.g., to discard units for common support). If specified, the
distance measure will not be used in matching.
a string containing a method for discarding units outside a
region of common support. Only allowed when
distance corresponds to a
discard is not
"none", whether to
re-estimate the propensity score in the remaining sample prior to matching.
the variable containing sampling weights to be incorporated into propensity score models and balance statistics.
the width of the caliper used for caliper matching. A caliper can only be placed on the propensity score.
logical; when a caliper is specified, whether it
is in standard deviation units (
TRUE) or raw units (
logical; whether information about the matching
process should be printed to the console.
additional arguments passed to
quickmatch::quickmatch(). Allowed arguments include
target, and other arguments passed to
quickmatch::quickmatch() for details). In particular, changing
seed_method from its default can improve performance.
No arguments will be passed to
antiexact are ignored with a warning.
Generalized full matching is similar to optimal full matching, but has some additional flexibility that can be controlled by some of the extra arguments available. By default,
method = "quick" performs a standard full match in which all units are matched (unless restricted by the caliper) and assigned to a subclass. Each subclass could contain multiple units from each treatment group. The subclasses are chosen to minimize the largest within-subclass distance between units (including between units of the same treatment group). Notably, generalized full matching requires less memory and can run much faster than optimal full matching and optimal pair matching and, in some cases, even than nearest neighbor matching, and it can be used with huge datasets (e.g., in the millions) while running in under a minute.
All outputs described in
matchit() are returned with
method = "quick" except for
match.matrix. This is because
matching strata are not indexed by treated units as they are in some other
forms of matching. When
include.obj = TRUE in the call to
matchit(), the output of the call to
quickmatch::quickmatch() will be
included in the output. When
exact is specified, this will be a list
of such objects, one for each stratum of the
In a manuscript, be sure to cite the quickmatch package if using
method = "quick":
Sävje, F., Sekhon, J., & Higgins, M. (2018). quickmatch: Quick generalized full matching. https://CRAN.R-project.org/package=quickmatch
For example, a sentence might read:
Generalized full matching was performed using the MatchIt package (Ho, Imai, King, & Stuart, 2011) in R, which calls functions from the quickmatch package (Savje, Sekhon, & Higgins, 2018).
You should also cite the following paper, which develops and describes the method:
Sävje, F., Higgins, M. J., & Sekhon, J. S. (2021). Generalized Full Matching. Political Analysis, 29(4), 423–447. doi:10.1017/pan.2020.32
data("lalonde") # Generalize full PS matching m.out1 <- matchit(treat ~ age + educ + race + nodegree + married + re74 + re75, data = lalonde, method = "quick") m.out1 #> A matchit object #> - method: Generalized full matching #> - distance: Propensity score #> - estimated with logistic regression #> - number of obs.: 614 (original), 614 (matched) #> - target estimand: ATT #> - covariates: age, educ, race, nodegree, married, re74, re75 summary(m.out1) #> #> Call: #> matchit(formula = treat ~ age + educ + race + nodegree + married + #> re74 + re75, data = lalonde, method = "quick") #> #> Summary of Balance for All Data: #> Means Treated Means Control Std. Mean Diff. Var. Ratio eCDF Mean #> distance 0.5774 0.1822 1.7941 0.9211 0.3774 #> age 25.8162 28.0303 -0.3094 0.4400 0.0813 #> educ 10.3459 10.2354 0.0550 0.4959 0.0347 #> raceblack 0.8432 0.2028 1.7615 . 0.6404 #> racehispan 0.0595 0.1422 -0.3498 . 0.0827 #> racewhite 0.0973 0.6550 -1.8819 . 0.5577 #> nodegree 0.7081 0.5967 0.2450 . 0.1114 #> married 0.1892 0.5128 -0.8263 . 0.3236 #> re74 2095.5737 5619.2365 -0.7211 0.5181 0.2248 #> re75 1532.0553 2466.4844 -0.2903 0.9563 0.1342 #> eCDF Max #> distance 0.6444 #> age 0.1577 #> educ 0.1114 #> raceblack 0.6404 #> racehispan 0.0827 #> racewhite 0.5577 #> nodegree 0.1114 #> married 0.3236 #> re74 0.4470 #> re75 0.2876 #> #> Summary of Balance for Matched Data: #> Means Treated Means Control Std. Mean Diff. Var. Ratio eCDF Mean #> distance 0.5774 0.5764 0.0047 0.9895 0.0044 #> age 25.8162 25.4476 0.0515 0.5099 0.0728 #> educ 10.3459 10.6781 -0.1652 0.5742 0.0251 #> raceblack 0.8432 0.8376 0.0155 . 0.0056 #> racehispan 0.0595 0.0607 -0.0052 . 0.0012 #> racewhite 0.0973 0.1017 -0.0148 . 0.0044 #> nodegree 0.7081 0.6602 0.1054 . 0.0479 #> married 0.1892 0.1355 0.1371 . 0.0537 #> re74 2095.5737 2873.4651 -0.1592 0.8886 0.0709 #> re75 1532.0553 1634.2731 -0.0318 1.8751 0.0736 #> eCDF Max Std. Pair Dist. #> distance 0.0541 0.0202 #> age 0.2659 1.2519 #> educ 0.0855 1.2506 #> raceblack 0.0056 0.0265 #> racehispan 0.0012 0.4970 #> racewhite 0.0044 0.3641 #> nodegree 0.0479 0.9324 #> married 0.0537 0.4870 #> re74 0.3004 0.8699 #> re75 0.2760 0.8428 #> #> Sample Sizes: #> Control Treated #> All 429. 185 #> Matched (ESS) 50.74 185 #> Matched 429. 185 #> Unmatched 0. 0 #> Discarded 0. 0 #>