matchit() is the main function of MatchIt and performs
pairing, subset selection, and subclassification with the aim of creating
treatment and control groups balanced on included covariates. MatchIt
implements the suggestions of Ho, Imai, King, and Stuart (2007) for
improving parametric statistical models by preprocessing data with
nonparametric matching methods.
This page documents the overall use of matchit(), but for specifics
of how matchit() works with individual matching methods, see the
individual pages linked in the Details section below.
Usage
matchit(
formula,
data = NULL,
method = "nearest",
distance = "glm",
link = "logit",
distance.options = list(),
estimand = "ATT",
exact = NULL,
mahvars = NULL,
antiexact = NULL,
discard = "none",
reestimate = FALSE,
s.weights = NULL,
replace = FALSE,
m.order = NULL,
caliper = NULL,
std.caliper = TRUE,
ratio = 1,
verbose = FALSE,
include.obj = FALSE,
normalize = TRUE,
...
)Arguments
- formula
a two-sided
formulaobject 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. The formula should be specified asA ~ X1 + X2 + ...whereArepresents the treatment variable andX1andX2are covariates.- data
a data frame containing the variables named in
formulaand possible other arguments. If not found indata, the variables will be sought in the environment.- method
the matching method to be used. The allowed methods are
"nearest"for nearest neighbor matching (on the propensity score by default),"optimal"for optimal pair matching,"full"for optimal full matching,"quick"for generalized (quick) full matching,"genetic"for genetic matching,"cem"for coarsened exact matching,"exact"for exact matching,"cardinality"for cardinality and profile matching, and"subclass"for subclassification. When set toNULL, no matching will occur, but propensity score estimation and common support restrictions will still occur if requested. See the linked pages for each method for more details on what these methods do, how the arguments below are used by each on, and what additional arguments are allowed.- distance
the distance measure to be used. Can be either the name of a method of estimating propensity scores (e.g.,
"glm"), the name of a method of computing a distance matrix from the covariates (e.g.,"mahalanobis"), a vector of already-computed distance measures, or a matrix of pairwise distances. Seedistancefor allowable options. The default is"glm"for propensity scores estimated with logistic regression usingglm(). Ignored for some methods; see individual methods pages for information on whether and how the distance measure is used.- link
when
distanceis specified as a string, an additional argument controlling the link function used in estimating the distance measure. Allowable options depend on the specificdistancevalue specified. Seedistancefor allowable options with each option. The default is"logit", which, along withdistance = "glm", identifies the default measure as logistic regression propensity scores.- distance.options
a named list containing additional arguments supplied to the function that estimates the distance measure as determined by the argument to
distance. Seedistancefor an example of its use.- estimand
a string containing the name of the target estimand desired. Can be one of
"ATT","ATC", or"ATE". Default is"ATT". See Details and the individual methods pages for information on how this argument is used.- exact
for methods that allow it, for which variables exact matching should take place. Can be specified as a string containing the names of variables in
datato be used or a one-sided formula with the desired variables on the right-hand side (e.g.,~ X3 + X4). See the individual methods pages for information on whether and how this argument is used.- mahvars
for methods that allow it, on which variables Mahalanobis distance matching should take place when
distancecorresponds to propensity scores. Usually used to perform Mahalanobis distance matching within propensity score calipers, where the propensity scores are computed usingformulaanddistance. Can be specified as a string containing the names of variables indatato be used or a one-sided formula with the desired variables on the right-hand side (e.g.,~ X3 + X4). See the individual methods pages for information on whether and how this argument is used.- antiexact
for methods that allow it, for which variables anti-exact matching should take place. Anti-exact matching ensures paired individuals do not have the same value of the anti-exact matching variable(s). Can be specified as a string containing the names of variables in
datato be used or a one-sided formula with the desired variables on the right-hand side (e.g.,~ X3 + X4). See the individual methods pages for information on whether and how this argument is used.- discard
a string containing a method for discarding units outside a region of common support. When a propensity score is estimated or supplied to
distanceas a vector, the options are"none","treated","control", or"both". For"none", no units are discarded for common support. Otherwise, units whose propensity scores fall outside the corresponding region are discarded. Can also be alogicalvector whereTRUEindicates the unit is to be discarded. Default is"none"for no common support restriction. See Details.- reestimate
if
discardis not"none"and propensity scores are estimated, whether to re-estimate the propensity scores in the remaining sample. Default isFALSEto use the propensity scores estimated in the original sample.- s.weights
an optional numeric vector of sampling weights to be incorporated into propensity score models and balance statistics. Can also be specified as a string containing the name of variable in
datato be used or a one-sided formula with the variable on the right-hand side (e.g.,~ SW). Not all propensity score models accept sampling weights; seedistancefor information on which do and do not, and seevignette("sampling-weights")for details on how to use sampling weights in a matching analysis.- replace
for methods that allow it, whether matching should be done with replacement (
TRUE), where control units are allowed to be matched to several treated units, or without replacement (FALSE), where control units can only be matched to one treated unit each. See the individual methods pages for information on whether and how this argument is used. Default isFALSEfor matching without replacement.- m.order
for methods that allow it, the order that the matching takes place. Allowable options depend on the matching method. The default of
NULLcorresponds to"largest"when a propensity score is estimated or supplied as a vector and"data"otherwise.- caliper
for methods that allow it, the width(s) of the caliper(s) to use in matching. Should be a numeric vector with each value named according to the variable to which the caliper applies. To apply to the distance measure, the value should be unnamed. See the individual methods pages for information on whether and how this argument is used. The default is
NULLfor no caliper.- std.caliper
logical; when a caliper is specified, whether the the caliper is in standard deviation units (TRUE) or raw units (FALSE). Can either be of length 1, applying to all calipers, or of length equal to the length ofcaliper. Default isTRUE.- ratio
for methods that allow it, how many control units should be matched to each treated unit in k:1 matching. Should be a single integer value. See the individual methods pages for information on whether and how this argument is used. The default is 1 for 1:1 matching.
- verbose
logical; whether information about the matching process should be printed to the console. What is printed depends on the matching method. Default isFALSEfor no printing other than warnings.- include.obj
logical; whether to include any objects created in the matching process in the output, i.e., by the functions from other packagesmatchit()calls. What is included depends on the matching method. Default isFALSE.- normalize
logical; whether to rescale the nonzero weights in each treatment group to have an average of 1. Default isTRUE. See "How Matching Weights Are Computed" below for more details.- ...
additional arguments passed to the functions used in the matching process. See the individual methods pages for information on what additional arguments are allowed for each method.
Value
When method is something other than "subclass", a
matchit object with the following components:
- match.matrix
a matrix containing the matches. The row names correspond to the treated units and the values in each row are the names (or indices) of the control units matched to each treated unit. When treated units are matched to different numbers of control units (e.g., with variable ratio matching or matching with a caliper), empty spaces will be filled with
NA. Not included whenmethodis"full","cem"(unlessk2k = TRUE),"exact","quick", or"cardinality"(unlessmahvarsis supplied andratiois an integer).- subclass
a factor containing matching pair/stratum membership for each unit. Unmatched units will have a value of
NA. Not included whenreplace = TRUEor whenmethod = "cardinality"unlessmahvarsis supplied andratiois an integer.- weights
a numeric vector of estimated matching weights. Unmatched and discarded units will have a weight of zero.
- model
the fit object of the model used to estimate propensity scores when
distanceis specified as a method of estimating propensity scores. Whenreestimate = TRUE, this is the model estimated after discarding units.- X
a data frame of covariates mentioned in
formula,exact,mahvars,caliper, andantiexact.- call
the
matchit()call.- info
information on the matching method and distance measures used.
- estimand
the argument supplied to
estimand.- formula
the
formulasupplied.- treat
a vector of treatment status converted to zeros (0) and ones (1) if not already in that format.
- distance
a vector of distance values (i.e., propensity scores) when
distanceis supplied as a method of estimating propensity scores or a numeric vector.- discarded
a logical vector denoting whether each observation was discarded (
TRUE) or not (FALSE) by the argument todiscard.- s.weights
the vector of sampling weights supplied to the
s.weightsargument, if any.- exact
a one-sided formula containing the variables, if any, supplied to
exact.- mahvars
a one-sided formula containing the variables, if any, supplied to
mahvars.- obj
when
include.obj = TRUE, an object containing the intermediate results of the matching procedure. See the individual methods pages for what this component will contain.
When method = "subclass", a matchit.subclass object with the same
components as above except that match.matrix is excluded and one
additional component, q.cut, is included, containing a vector of the
distance measure cutpoints used to define the subclasses. See
method_subclass for details.
Details
Details for the various matching methods can be found at the following help pages:
method_nearestfor nearest neighbor matchingmethod_optimalfor optimal pair matchingmethod_fullfor optimal full matchingmethod_quickfor generalized (quick) full matchingmethod_geneticfor genetic matchingmethod_cemfor coarsened exact matchingmethod_exactfor exact matchingmethod_cardinalityfor cardinality and profile matchingmethod_subclassfor subclassification
The pages contain information on what the method does, which of the arguments above are
allowed with them and how they are interpreted, and what additional
arguments can be supplied to further tune the method. Note that the default
method with no arguments supplied other than formula and data
is 1:1 nearest neighbor matching without replacement on a propensity score
estimated using a logistic regression of the treatment on the covariates.
This is not the same default offered by other matching programs, such as
those in Matching, teffects in Stata, or PROC PSMATCH
in SAS, so care should be taken if trying to replicate the results of those
programs.
When method = NULL, no matching will occur, but any propensity score
estimation and common support restriction will. This can be a simple way to
estimate the propensity score for use in future matching specifications
without having to re-estimate it each time. The matchit() output with
no matching can be supplied to summary() to examine balance prior to
matching on any of the included covariates and on the propensity score if
specified. All arguments other than distance, discard, and
reestimate will be ignored.
See distance for details on the several ways to
specify the distance, link, and distance.options
arguments to estimate propensity scores and create distance measures.
When the treatment variable is not a 0/1 variable, it will be coerced
to one and returned as such in the matchit() output (see section
Value, below). The following rules are used: 1) if 0 is one of the
values, it will be considered the control and the other value the treated;
2) otherwise, if the variable is a factor, levels(treat)[1] will be
considered control and the other value the treated; 3) otherwise,
sort(unique(treat))[1] will be considered control and the other value
the treated. It is safest to ensure the treatment variable is a 0/1
variable.
The discard option implements a common support restriction. It can
only be used when a distance measure is an estimated propensity score or supplied as a vector and is ignored for some matching
methods. When specified as "treated", treated units whose distance
measure is outside the range of distance measures of the control units will
be discarded. When specified as "control", control units whose
distance measure is outside the range of distance measures of the treated
units will be discarded. When specified as "both", treated and
control units whose distance measure is outside the intersection of the
range of distance measures of the treated units and the range of distance
measures of the control units will be discarded. When reestimate = TRUE and distance corresponds to a propensity score-estimating
function, the propensity scores are re-estimated in the remaining units
prior to being used for matching or calipers.
Caution should be used when interpreting effects estimated with various
values of estimand. Setting estimand = "ATT" doesn't
necessarily mean the average treatment effect in the treated is being
estimated; it just means that for matching methods, treated units will be
untouched and given weights of 1 and control units will be matched to them
(and the opposite for estimand = "ATC"). If a caliper is supplied or
treated units are removed for common support or some other reason (e.g.,
lacking matches when using exact matching), the actual estimand targeted is
not the ATT but the treatment effect in the matched sample. The argument to
estimand simply triggers which units are matched to which, and for
stratification-based methods (exact matching, CEM, full matching, and
subclassification), determines the formula used to compute the
stratification weights.
How Matching Weights Are Computed
Matching weights are computed in one of two ways depending on whether matching was done with replacement or not.
Matching without replacement and subclassification
For matching without replacement (except for cardinality matching), including subclassification, each
unit is assigned to a subclass, which represents the pair they are a part of
(in the case of k:1 matching) or the stratum they belong to (in the case of
exact matching, coarsened exact matching, full matching, or
subclassification). The formula for computing the weights depends on the
argument supplied to estimand. A new "stratum propensity score"
(\(p^s_i\)) is computed for each unit \(i\) as \(p^s_i = \frac{1}{n_s}\sum_{j: s_j =s_i}{I(A_j=1)}\) where \(n_s\) is the size of subclass \(s\) and \(I(A_j=1)\) is 1 if unit \(j\) is treated and 0 otherwise. That is, the stratum propensity score for stratum \(s\) is the proportion of units in stratum \(s\) that are
in the treated group, and all units in stratum \(s\) are assigned that
stratum propensity score. This is distinct from the propensity score used for matching, if any. Weights are then computed using the standard formulas for
inverse probability weights with the stratum propensity score inserted:
for the ATT, weights are 1 for the treated units and \(\frac{p^s}{1-p^s}\) for the control units
for the ATC, weights are \(\frac{1-p^s}{p^s}\) for the treated units and 1 for the control units
for the ATE, weights are \(\frac{1}{p^s}\) for the treated units and \(\frac{1}{1-p^s}\) for the control units.
For cardinality matching, all matched units receive a weight of 1.
Matching witht replacement
For matching with replacement, units are not assigned to unique strata. For
the ATT, each treated unit gets a weight of 1. Each control unit is weighted
as the sum of the inverse of the number of control units matched to the same
treated unit across its matches. For example, if a control unit was matched
to a treated unit that had two other control units matched to it, and that
same control was matched to a treated unit that had one other control unit
matched to it, the control unit in question would get a weight of \(1/3 + 1/2 = 5/6\). For the ATC, the same is true with the treated and control labels
switched. The weights are computed using the match.matrix component
of the matchit() output object.
Normalized weights
When normalize = TRUE (the default), in each treatment group, weights are divided by the mean of the nonzero
weights in that treatment group to make the weights sum to the number of
units in that treatment group (i.e., to have an average of 1).
Sampling weights
If sampling weights are included through the
s.weights argument, they will be included in the matchit()
output object but not incorporated into the matching weights.
match.data(), which extracts the matched set from a matchit object,
combines the matching weights and sampling weights.
References
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007). Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis, 15(3), 199–236. doi:10.1093/pan/mpl013
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2011). MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. Journal of Statistical Software, 42(8). doi:10.18637/jss.v042.i08
See also
summary.matchit() for balance assessment after matching, plot.matchit() for plots of covariate balance and propensity score overlap after matching.
vignette("MatchIt")for an introduction to matching with MatchItvignette("matching-methods")for descriptions of the variety of matching methods and options availablevignette("assessing-balance")for information on assessing the quality of a matching specificationvignette("estimating-effects")for instructions on how to estimate treatment effects after matchingvignette("sampling-weights")for a guide to using MatchIt with sampling weights.
Author
Daniel Ho, Kosuke Imai, Gary King, and Elizabeth Stuart wrote the original package. Starting with version 4.0.0, Noah Greifer is the primary maintainer and developer.
Examples
data("lalonde")
# Default: 1:1 NN PS matching w/o replacement
m.out1 <- matchit(treat ~ age + educ + race + nodegree +
married + re74 + re75, data = lalonde)
m.out1
#> A `matchit` object
#> - method: 1:1 nearest neighbor matching without replacement
#> - distance: Propensity score - estimated with logistic regression
#> - number of obs.: 614 (original), 370 (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)
#>
#> 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 0.3629 0.9739 0.7566 0.1321
#> age 25.8162 25.3027 0.0718 0.4568 0.0847
#> educ 10.3459 10.6054 -0.1290 0.5721 0.0239
#> raceblack 0.8432 0.4703 1.0259 . 0.3730
#> racehispan 0.0595 0.2162 -0.6629 . 0.1568
#> racewhite 0.0973 0.3135 -0.7296 . 0.2162
#> nodegree 0.7081 0.6378 0.1546 . 0.0703
#> married 0.1892 0.2108 -0.0552 . 0.0216
#> re74 2095.5737 2342.1076 -0.0505 1.3289 0.0469
#> re75 1532.0553 1614.7451 -0.0257 1.4956 0.0452
#> eCDF Max Std. Pair Dist.
#> distance 0.4216 0.9740
#> age 0.2541 1.3938
#> educ 0.0757 1.2474
#> raceblack 0.3730 1.0259
#> racehispan 0.1568 1.0743
#> racewhite 0.2162 0.8390
#> nodegree 0.0703 1.0106
#> married 0.0216 0.8281
#> re74 0.2757 0.7965
#> re75 0.2054 0.7381
#>
#> Sample Sizes:
#> Control Treated
#> All 429 185
#> Matched 185 185
#> Unmatched 244 0
#> Discarded 0 0
#>
# 1:1 NN Mahalanobis distance matching w/ replacement and
# exact matching on married and race
m.out2 <- matchit(treat ~ age + educ + race + nodegree +
married + re74 + re75, data = lalonde,
distance = "mahalanobis", replace = TRUE,
exact = ~ married + race)
m.out2
#> A `matchit` object
#> - method: 1:1 nearest neighbor matching with replacement
#> - distance: Mahalanobis - number of obs.: 614 (original), 265 (matched)
#> - target estimand: ATT
#> - covariates: age, educ, race, nodegree, married, re74, re75
summary(m.out2, un = TRUE)
#>
#> Call:
#> matchit(formula = treat ~ age + educ + race + nodegree + married +
#> re74 + re75, data = lalonde, distance = "mahalanobis", exact = ~married +
#> race, replace = TRUE)
#>
#> Summary of Balance for All Data:
#> Means Treated Means Control Std. Mean Diff. Var. Ratio eCDF Mean
#> 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
#> 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
#> age 25.8162 25.6162 0.0280 0.6513 0.0466
#> educ 10.3459 10.3946 -0.0242 1.1564 0.0065
#> raceblack 0.8432 0.8432 0.0000 . 0.0000
#> racehispan 0.0595 0.0595 0.0000 . 0.0000
#> racewhite 0.0973 0.0973 0.0000 . 0.0000
#> nodegree 0.7081 0.7135 -0.0119 . 0.0054
#> married 0.1892 0.1892 0.0000 . 0.0000
#> re74 2095.5737 1861.6424 0.0479 1.4978 0.0286
#> re75 1532.0553 1091.6516 0.1368 2.0335 0.0347
#> eCDF Max Std. Pair Dist.
#> age 0.1784 0.4918
#> educ 0.0324 0.2070
#> raceblack 0.0000 0.0000
#> racehispan 0.0000 0.0000
#> racewhite 0.0000 0.0000
#> nodegree 0.0054 0.0119
#> married 0.0000 0.0000
#> re74 0.1784 0.2606
#> re75 0.0811 0.2445
#>
#> Sample Sizes:
#> Control Treated
#> All 429. 185
#> Matched (ESS) 34.89 185
#> Matched 80. 185
#> Unmatched 349. 0
#> Discarded 0. 0
#>
# 2:1 NN Mahalanobis distance matching within caliper defined
# by a probit pregression PS
m.out3 <- matchit(treat ~ age + educ + race + nodegree +
married + re74 + re75, data = lalonde,
distance = "glm", link = "probit",
mahvars = ~ age + educ + re74 + re75,
caliper = .1, ratio = 2)
m.out3
#> A `matchit` object
#> - method: 2:1 nearest neighbor matching without replacement
#> - distance: Mahalanobis [matching]
#> Propensity score [caliper]
#> - estimated with probit regression
#> - caliper: <distance> (0.029)
#> - number of obs.: 614 (original), 257 (matched)
#> - target estimand: ATT
#> - covariates: age, educ, race, nodegree, married, re74, re75
summary(m.out3, un = TRUE)
#>
#> Call:
#> matchit(formula = treat ~ age + educ + race + nodegree + married +
#> re74 + re75, data = lalonde, distance = "glm", link = "probit",
#> mahvars = ~age + educ + re74 + re75, caliper = 0.1, ratio = 2)
#>
#> Summary of Balance for All Data:
#> Means Treated Means Control Std. Mean Diff. Var. Ratio eCDF Mean
#> distance 0.5773 0.1817 1.8276 0.8777 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 0.6413
#> 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 0.5113 0.4887 0.1041 1.0555 0.0283
#> age 26.0721 24.6934 0.1927 0.4630 0.0866
#> educ 10.4144 10.3466 0.0337 0.6141 0.0187
#> raceblack 0.7387 0.7135 0.0695 . 0.0253
#> racehispan 0.0991 0.1014 -0.0095 . 0.0023
#> racewhite 0.1622 0.1852 -0.0777 . 0.0230
#> nodegree 0.6667 0.6451 0.0473 . 0.0215
#> married 0.1892 0.2282 -0.0997 . 0.0390
#> re74 3016.7936 2196.5637 0.1679 2.0248 0.0590
#> re75 2023.1731 1514.1173 0.1581 2.1614 0.0441
#> eCDF Max Std. Pair Dist.
#> distance 0.1441 0.6220
#> age 0.3198 1.1794
#> educ 0.0633 0.8039
#> raceblack 0.0253 0.5840
#> racehispan 0.0023 0.4924
#> racewhite 0.0230 0.6009
#> nodegree 0.0215 0.7231
#> married 0.0390 0.7520
#> re74 0.2220 0.9805
#> re75 0.0978 2.1771
#>
#> Sample Sizes:
#> Control Treated
#> All 429. 185
#> Matched (ESS) 111.98 111
#> Matched 146. 111
#> Unmatched 283. 74
#> Discarded 0. 0
#>
# Optimal full PS matching for the ATE within calipers on
# PS, age, and educ
m.out4 <- matchit(treat ~ age + educ + race + nodegree +
married + re74 + re75, data = lalonde,
method = "full", estimand = "ATE",
caliper = c(.1, age = 2, educ = 1),
std.caliper = c(TRUE, FALSE, FALSE))
m.out4
#> A `matchit` object
#> - method: Optimal full matching
#> - distance: Propensity score [caliper]
#> - estimated with logistic regression
#> - caliper: <distance> (0.029), age (2), educ (1)
#> - number of obs.: 614 (original), 314 (matched)
#> - target estimand: ATE
#> - covariates: age, educ, race, nodegree, married, re74, re75
summary(m.out4, un = TRUE)
#>
#> Call:
#> matchit(formula = treat ~ age + educ + race + nodegree + married +
#> re74 + re75, data = lalonde, method = "full", estimand = "ATE",
#> caliper = c(0.1, age = 2, educ = 1), std.caliper = c(TRUE,
#> FALSE, FALSE))
#>
#> Summary of Balance for All Data:
#> Means Treated Means Control Std. Mean Diff. Var. Ratio eCDF Mean
#> distance 0.5774 0.1822 1.7569 0.9211 0.3774
#> age 25.8162 28.0303 -0.2419 0.4400 0.0813
#> educ 10.3459 10.2354 0.0448 0.4959 0.0347
#> raceblack 0.8432 0.2028 1.6708 . 0.6404
#> racehispan 0.0595 0.1422 -0.2774 . 0.0827
#> racewhite 0.0973 0.6550 -1.4080 . 0.5577
#> nodegree 0.7081 0.5967 0.2355 . 0.1114
#> married 0.1892 0.5128 -0.7208 . 0.3236
#> re74 2095.5737 5619.2365 -0.5958 0.5181 0.2248
#> re75 1532.0553 2466.4844 -0.2870 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 0.3515 0.3473 0.0185 1.0175 0.0146
#> age 22.4064 22.0133 0.0430 0.8438 0.0156
#> educ 10.7009 10.6440 0.0230 1.0814 0.0107
#> raceblack 0.4618 0.4586 0.0083 . 0.0032
#> racehispan 0.1497 0.1083 0.1387 . 0.0414
#> racewhite 0.3885 0.4331 -0.1125 . 0.0446
#> nodegree 0.5792 0.5824 -0.0068 . 0.0032
#> married 0.2894 0.2677 0.0482 . 0.0217
#> re74 2679.9337 2952.3545 -0.0461 1.0852 0.0401
#> re75 1430.9478 1745.5573 -0.0966 1.2043 0.0535
#> eCDF Max Std. Pair Dist.
#> distance 0.0625 0.0449
#> age 0.1078 0.1260
#> educ 0.0594 0.2067
#> raceblack 0.0032 0.0325
#> racehispan 0.0414 0.5148
#> racewhite 0.0446 0.3771
#> nodegree 0.0032 0.2018
#> married 0.0217 0.5360
#> re74 0.1923 0.5470
#> re75 0.1493 0.6744
#>
#> Sample Sizes:
#> Control Treated
#> All 429. 185.
#> Matched (ESS) 133.1 39.37
#> Matched 203. 111.
#> Unmatched 226. 74.
#> Discarded 0. 0.
#>
# Subclassification on a logistic PS with 10 subclasses after
# discarding controls outside common support of PS
s.out1 <- matchit(treat ~ age + educ + race + nodegree +
married + re74 + re75, data = lalonde,
method = "subclass", distance = "glm",
discard = "control", subclass = 10)
s.out1
#> A `matchit` object
#> - method: Subclassification (10 subclasses)
#> - distance: Propensity score [common support]
#> - estimated with logistic regression
#> - common support: control units dropped
#> - number of obs.: 614 (original), 557 (matched)
#> - target estimand: ATT
#> - covariates: age, educ, race, nodegree, married, re74, re75
summary(s.out1, un = TRUE)
#>
#> Call:
#> matchit(formula = treat ~ age + educ + race + nodegree + married +
#> re74 + re75, data = lalonde, method = "subclass", distance = "glm",
#> discard = "control", subclass = 10)
#>
#> 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 Across Subclasses
#> Means Treated Means Control Std. Mean Diff. Var. Ratio eCDF Mean
#> distance 0.5774 0.5710 0.0293 0.9338 0.0158
#> age 25.8162 25.3714 0.0622 0.4577 0.0866
#> educ 10.3459 10.4094 -0.0316 0.6894 0.0150
#> raceblack 0.8432 0.8262 0.0469 . 0.0170
#> racehispan 0.0595 0.0676 -0.0343 . 0.0081
#> racewhite 0.0973 0.1062 -0.0302 . 0.0089
#> nodegree 0.7081 0.6782 0.0658 . 0.0299
#> married 0.1892 0.1785 0.0274 . 0.0107
#> re74 2095.5737 2232.5096 -0.0280 1.3102 0.0449
#> re75 1532.0553 1643.4179 -0.0346 1.4216 0.0472
#> eCDF Max
#> distance 0.0541
#> age 0.3043
#> educ 0.0425
#> raceblack 0.0170
#> racehispan 0.0081
#> racewhite 0.0089
#> nodegree 0.0299
#> married 0.0107
#> re74 0.2731
#> re75 0.1841
#>
#> Sample Sizes:
#> Control Treated
#> All 429. 185
#> Matched (ESS) 72.59 185
#> Matched 372. 185
#> Unmatched 0. 0
#> Discarded 57. 0
#>