Computes and prints balance statistics for matchit and
matchit.subclass objects. Balance should be assessed to ensure the
matching or subclassification was effective at eliminating treatment group
imbalance and should be reported in the write-up of the results of the
analysis.
Usage
# S3 method for matchit
summary(
object,
interactions = FALSE,
addlvariables = NULL,
standardize = TRUE,
data = NULL,
pair.dist = TRUE,
un = TRUE,
improvement = FALSE,
...
)
# S3 method for matchit.subclass
summary(
object,
interactions = FALSE,
addlvariables = NULL,
standardize = TRUE,
data = NULL,
pair.dist = FALSE,
subclass = FALSE,
un = TRUE,
improvement = FALSE,
...
)
# S3 method for summary.matchit
print(x, digits = max(3, getOption("digits") - 3), ...)Arguments
- object
a
matchitobject; the output of a call tomatchit().- interactions
logical; whether to compute balance statistics for two-way interactions and squares of covariates. Default isFALSE.- addlvariables
additional variable for which balance statistics are to be computed along with the covariates in the
matchitobject. Can be entered in one of three ways: as a data frame of covariates with as many rows as there were units in the originalmatchit()call, as a string containing the names of variables indata, or as a right-sidedformulawith the additional variables (and possibly their transformations) found indata, the environment, or thematchitobject. Balance on squares and interactions of the additional variables will be included ifinteractions = TRUE.- standardize
logical; whether to compute standardized (TRUE) or unstandardized (FALSE) statistics. The standardized statistics are the standardized mean difference and the mean and maximum of the difference in the (weighted) empirical cumulative distribution functions (ECDFs). The unstandardized statistics are the raw mean difference and the mean and maximum of the quantile-quantile (QQ) difference. Variance ratios are produced either way. See Details below. Default isTRUE.- data
a optional data frame containing variables named in
addlvariablesif specified as a string or formula.- pair.dist
logical; whether to compute average absolute pair distances. For matching methods that don't include amatch.matrixcomponent in the output (i.e., exact matching, coarsened exact matching, full matching, and subclassification), computing pair differences can take a long time, especially for large datasets and with many covariates. For other methods (i.e., nearest neighbor, optimal, and genetic matching), computation is fairly quick. Default isFALSEfor subclassification andTRUEotherwise.- un
logical; whether to compute balance statistics for the unmatched sample. DefaultTRUE; set toFALSEfor more concise output.- improvement
logical; whether to compute the percent reduction in imbalance. DefaultFALSE. Ignored ifun = FALSE.- ...
ignored.
- subclass
after subclassification, whether to display balance for individual subclasses, and, if so, for which ones. Can be
TRUE(display balance for all subclasses),FALSE(display balance only in aggregate), or the indices (e.g.,1:6) of the specific subclasses for which to display balance. When anything other thanFALSE, aggregate balance statistics will not be displayed. Default isFALSE.- x
a
summay.matchitorsummary.matchit.subclassobject; the output of a call tosummary().- digits
the number of digits to round balance statistics to.
Value
For matchit objects, a summary.matchit object, which
is a list with the following components:
- call
the original call to
matchit()- nn
a matrix of the sample sizes in the original (unmatched) and matched samples
- sum.all
if
un = TRUE, a matrix of balance statistics for each covariate in the original (unmatched) sample- sum.matched
a matrix of balance statistics for each covariate in the matched sample
- reduction
if
improvement = TRUE, a matrix of the percent reduction in imbalance for each covariate in the matched sample
For match.subclass objects, a summary.matchit.subclass object,
which is a list as above containing the following components:
- call
the original call to
matchit()- sum.all
if
un = TRUE, a matrix of balance statistics for each covariate in the original sample- sum.subclass
if
subclassis notFALSE, a list of matrices of balance statistics for each subclass- sum.across
a matrix of balance statistics for each covariate computed using the subclassification weights
- reduction
if
improvement = TRUE, a matrix of the percent reduction in imbalance for each covariate in the matched sample- qn
a matrix of sample sizes within each subclass
- nn
a matrix of the sample sizes in the original (unmatched) and matched samples
Details
summary() computes a balance summary of a matchit object. This
include balance before and after matching or subclassification, as well as
the percent improvement in balance. The variables for which balance
statistics are computed are those included in the formula,
exact, and mahvars arguments to matchit(), as well as the
distance measure if distance is was supplied as a numeric vector or
method of estimating propensity scores. The X component of the
matchit object is used to supply the covariates.
The standardized mean differences are computed both before and after
matching or subclassification as the difference in treatment group means
divided by a standardization factor computed in the unmatched (original)
sample. The standardization factor depends on the argument supplied to
estimand in matchit(): for "ATT", it is the standard
deviation in the treated group; for "ATC", it is the standard
deviation in the control group; for "ATE", it is the square root of
the average of the variances within each treatment group. The post-matching
mean difference is computed with weighted means in the treatment groups
using the matching or subclassification weights.
The variance ratio is computed as the ratio of the treatment group
variances. Variance ratios are not computed for binary variables because
their variance is a function solely of their mean. After matching, weighted
variances are computed using the formula used in cov.wt(). The percent
reduction in bias is computed using the log of the variance ratios.
The eCDF difference statistics are computed by creating a (weighted) eCDF for each group and taking the difference between them for each covariate value. The eCDF is a function that outputs the (weighted) proportion of units with covariate values at or lower than the input value. The maximum eCDF difference is the same thing as the Kolmogorov-Smirnov statistic. The values are bounded at zero and one, with values closer to zero indicating good overlap between the covariate distributions in the treated and control groups. For binary variables, all eCDF differences are equal to the (weighted) difference in proportion and are computed that way.
The QQ difference statistics are computed by creating two samples of the same size by interpolating the values of the larger one. The values are arranged in order for each sample. The QQ difference for each quantile is the difference between the observed covariate values at that quantile between the two groups. The difference is on the scale of the original covariate. Values close to zero indicate good overlap between the covariate distributions in the treated and control groups. A weighted interpolation is used for post-matching QQ differences. For binary variables, all QQ differences are equal to the (weighted) difference in proportion and are computed that way.
The pair distance is the average of the absolute differences of a variable
between pairs. For example, if a treated unit was paired with four control
units, that set of units would contribute four absolute differences to the
average. Within a subclass, each combination of treated and control unit
forms a pair that contributes once to the average. The pair distance is
described in Stuart and Green (2008) and is the value that is minimized when
using optimal (full) matching. When standardize = TRUE, the
standardized versions of the variables are used, where the standardization
factor is as described above for the standardized mean differences. Pair
distances are not computed in the unmatched sample (because there are no
pairs). Because pair distance can take a while to compute, especially with
large datasets or for many covariates, setting pair.dist = FALSE is
one way to speed up summary().
The effective sample size (ESS) is a measure of the size of a hypothetical unweighted sample with roughly the same precision as a weighted sample. When non-uniform matching weights are computed (e.g., as a result of full matching, matching with replacement, or subclassification), the ESS can be used to quantify the potential precision remaining in the matched sample. The ESS will always be less than or equal to the matched sample size, reflecting the loss in precision due to using the weights. With non-uniform weights, it is printed in the sample size table; otherwise, it is removed because it does not contain additional information above the matched sample size.
After subclassification, the aggregate balance statistics are computed using the subclassification weights rather than averaging across subclasses.
All balance statistics (except pair differences) are computed incorporating
the sampling weights supplied to matchit(), if any. The unadjusted
balance statistics include the sampling weights and the adjusted balance
statistics use the matching weights multiplied by the sampling weights.
When printing, NA values are replaced with periods (.), and
the pair distance column in the unmatched and percent balance improvement
components of the output are omitted.
See also
summary() for the generic method; plot.summary.matchit() for
making a Love plot from summary() output.
cobalt::bal.tab.matchit(), which also displays balance for matchit
objects.
Examples
data("lalonde")
m.out <- matchit(treat ~ age + educ + married +
race + re74, data = lalonde,
method = "nearest", exact = ~ married,
replace = TRUE)
summary(m.out, interactions = TRUE)
#>
#> Call:
#> matchit(formula = treat ~ age + educ + married + race + re74,
#> data = lalonde, method = "nearest", exact = ~married, replace = TRUE)
#>
#> Summary of Balance for All Data:
#> Means Treated Means Control Std. Mean Diff. Var. Ratio
#> distance 0.5710 0.1850 1.7864 0.8760
#> age 25.8162 28.0303 -0.3094 0.4400
#> educ 10.3459 10.2354 0.0550 0.4959
#> married 0.1892 0.5128 -0.8263 .
#> raceblack 0.8432 0.2028 1.7615 .
#> racehispan 0.0595 0.1422 -0.3498 .
#> racewhite 0.0973 0.6550 -1.8819 .
#> re74 2095.5737 5619.2365 -0.7211 0.5181
#> age² 717.3946 901.7786 -0.4276 0.3627
#> age * educ 266.9784 282.3636 -0.1663 0.4912
#> age * married 5.5568 16.4872 -0.9147 0.4615
#> age * raceblack 21.9081 5.2867 1.4327 1.0055
#> age * racehispan 1.3568 3.7646 -0.4368 0.3127
#> age * racewhite 2.5514 18.9790 -2.0322 0.2424
#> age * re74 54074.0365 185650.1507 -0.9974 0.2539
#> educ² 111.0595 112.8974 -0.0468 0.5173
#> educ * married 1.9622 5.0816 -0.7475 0.5927
#> educ * raceblack 8.6973 2.0466 1.5803 0.9801
#> educ * racehispan 0.5784 1.2634 -0.2940 0.4869
#> educ * racewhite 1.0703 6.9254 -1.7671 0.3652
#> educ * re74 22898.7264 60430.2774 -0.6539 0.5188
#> married * raceblack 0.1568 0.0583 0.2709 .
#> married * racehispan 0.0162 0.0676 -0.4068 .
#> married * racewhite 0.0162 0.3869 -2.9352 .
#> married * re74 760.6329 4324.5356 -0.9734 0.2918
#> raceblack * re74 1817.2003 632.1307 0.2490 3.1701
#> racehispan * re74 151.3968 678.4817 -0.4400 0.1844
#> racewhite * re74 126.9766 4308.6240 -4.5164 0.0198
#> re74² 28141411.5686 77555527.0664 -0.4331 0.6548
#> eCDF Mean eCDF Max
#> distance 0.3765 0.6419
#> age 0.0813 0.1577
#> educ 0.0347 0.1114
#> married 0.3236 0.3236
#> raceblack 0.6404 0.6404
#> racehispan 0.0827 0.0827
#> racewhite 0.5577 0.5577
#> re74 0.2248 0.4470
#> age² 0.0813 0.1577
#> age * educ 0.0570 0.1187
#> age * married 0.1517 0.3236
#> age * raceblack 0.1831 0.6521
#> age * racehispan 0.0396 0.0827
#> age * racewhite 0.1966 0.5577
#> age * re74 0.2338 0.4470
#> educ² 0.0347 0.1114
#> educ * married 0.1732 0.3166
#> educ * raceblack 0.3537 0.6451
#> educ * racehispan 0.0457 0.0781
#> educ * racewhite 0.2791 0.5554
#> educ * re74 0.2185 0.4400
#> married * raceblack 0.0985 0.0985
#> married * racehispan 0.0514 0.0514
#> married * racewhite 0.3707 0.3707
#> married * re74 0.1889 0.3626
#> raceblack * re74 0.0861 0.1523
#> racehispan * re74 0.0405 0.0794
#> racewhite * re74 0.2539 0.4959
#> re74² 0.2248 0.4470
#>
#> Summary of Balance for Matched Data:
#> Means Treated Means Control Std. Mean Diff. Var. Ratio
#> distance 0.5710 0.5705 0.0023 0.9769
#> age 25.8162 25.9568 -0.0196 0.4500
#> educ 10.3459 10.6054 -0.1290 0.5297
#> married 0.1892 0.1892 0.0000 .
#> raceblack 0.8432 0.8324 0.0297 .
#> racehispan 0.0595 0.0595 0.0000 .
#> racewhite 0.0973 0.1081 -0.0365 .
#> re74 2095.5737 1882.1670 0.0437 1.5002
#> age² 717.3946 785.1351 -0.1571 0.3693
#> age * educ 266.9784 268.1514 -0.0127 0.6110
#> age * married 5.5568 5.3459 0.0176 1.0543
#> age * raceblack 21.9081 21.1243 0.0676 0.7602
#> age * racehispan 1.3568 1.7297 -0.0677 0.5429
#> age * racewhite 2.5514 3.1027 -0.0682 0.6518
#> age * re74 54074.0365 56189.4636 -0.0160 0.9095
#> educ² 111.0595 119.9459 -0.2261 0.5337
#> educ * married 1.9622 2.0432 -0.0194 0.9014
#> educ * raceblack 8.6973 8.7730 -0.0180 0.7955
#> educ * racehispan 0.5784 0.6054 -0.0116 0.8764
#> educ * racewhite 1.0703 1.2270 -0.0473 0.8114
#> educ * re74 22898.7264 21438.6573 0.0254 1.4256
#> married * raceblack 0.1568 0.1514 0.0149 .
#> married * racehispan 0.0162 0.0216 -0.0428 .
#> married * racewhite 0.0162 0.0162 0.0000 .
#> married * re74 760.6329 626.0440 0.0368 2.2366
#> raceblack * re74 1817.2003 1629.7393 0.0394 1.4586
#> racehispan * re74 151.3968 90.2110 0.0511 3.1377
#> racewhite * re74 126.9766 162.2167 -0.0381 1.0624
#> re74² 28141411.5686 19125662.0900 0.0790 2.9783
#> eCDF Mean eCDF Max Std. Pair Dist.
#> distance 0.0032 0.0432 0.0165
#> age 0.0765 0.2216 1.1241
#> educ 0.0353 0.1892 0.9194
#> married 0.0000 0.0000 0.0000
#> raceblack 0.0108 0.0108 0.0297
#> racehispan 0.0000 0.0000 0.0216
#> racewhite 0.0108 0.0108 0.0365
#> re74 0.0544 0.2919 0.3888
#> age² 0.0765 0.2216 1.1358
#> age * educ 0.0342 0.0973 0.6831
#> age * married 0.0082 0.0649 0.1172
#> age * raceblack 0.0611 0.1946 0.5549
#> age * racehispan 0.0112 0.0270 0.2265
#> age * racewhite 0.0132 0.0270 0.1698
#> age * re74 0.0551 0.2919 0.4300
#> educ² 0.0353 0.1892 0.9382
#> educ * married 0.0081 0.0811 0.0816
#> educ * raceblack 0.0337 0.1568 0.3956
#> educ * racehispan 0.0047 0.0216 0.1415
#> educ * racewhite 0.0079 0.0216 0.0963
#> educ * re74 0.0507 0.2919 0.4119
#> married * raceblack 0.0054 0.0054 0.0149
#> married * racehispan 0.0054 0.0054 0.0428
#> married * racewhite 0.0000 0.0000 0.0000
#> married * re74 0.0129 0.0865 0.1146
#> raceblack * re74 0.0448 0.2486 0.3194
#> racehispan * re74 0.0055 0.0162 0.1888
#> racewhite * re74 0.0069 0.0378 0.1735
#> re74² 0.0544 0.2919 0.2207
#>
#> Sample Sizes:
#> Control Treated
#> All 429. 185
#> Matched (ESS) 47.6 185
#> Matched 86. 185
#> Unmatched 343. 0
#> Discarded 0. 0
#>
s.out <- matchit(treat ~ age + educ + married +
race + nodegree + re74 + re75,
data = lalonde, method = "subclass")
summary(s.out, addlvariables = ~log(age) + I(re74==0))
#>
#> Call:
#> matchit(formula = treat ~ age + educ + married + race + nodegree +
#> re74 + re75, data = lalonde, method = "subclass")
#>
#> Summary of Balance for All Data:
#> Means Treated Means Control Std. Mean Diff. Var. Ratio
#> distance 0.5774 0.1822 1.7941 0.9211
#> age 25.8162 28.0303 -0.3094 0.4400
#> educ 10.3459 10.2354 0.0550 0.4959
#> married 0.1892 0.5128 -0.8263 .
#> raceblack 0.8432 0.2028 1.7615 .
#> racehispan 0.0595 0.1422 -0.3498 .
#> racewhite 0.0973 0.6550 -1.8819 .
#> nodegree 0.7081 0.5967 0.2450 .
#> re74 2095.5737 5619.2365 -0.7211 0.5181
#> re75 1532.0553 2466.4844 -0.2903 0.9563
#> log(age) 3.2167 3.2659 -0.1913 0.5093
#> I(re74 == 0)TRUE 0.7081 0.2611 0.9833 .
#> eCDF Mean eCDF Max
#> distance 0.3774 0.6444
#> age 0.0813 0.1577
#> educ 0.0347 0.1114
#> married 0.3236 0.3236
#> raceblack 0.6404 0.6404
#> racehispan 0.0827 0.0827
#> racewhite 0.5577 0.5577
#> nodegree 0.1114 0.1114
#> re74 0.2248 0.4470
#> re75 0.1342 0.2876
#> log(age) 0.0813 0.1577
#> I(re74 == 0)TRUE 0.4470 0.4470
#>
#> Summary of Balance Across Subclasses
#> Means Treated Means Control Std. Mean Diff. Var. Ratio
#> distance 0.5774 0.5610 0.0744 0.8183
#> age 25.8162 26.2522 -0.0609 0.4375
#> educ 10.3459 10.2809 0.0323 0.6392
#> married 0.1892 0.2425 -0.1361 .
#> raceblack 0.8432 0.8279 0.0423 .
#> racehispan 0.0595 0.0360 0.0990 .
#> racewhite 0.0973 0.1361 -0.1309 .
#> nodegree 0.7081 0.6984 0.0214 .
#> re74 2095.5737 2811.9421 -0.1466 0.8806
#> re75 1532.0553 1834.5600 -0.0940 1.2233
#> log(age) 3.2167 3.1959 0.0808 0.4883
#> I(re74 == 0)TRUE 0.7081 0.4443 0.5803 .
#> eCDF Mean eCDF Max
#> distance 0.0366 0.0815
#> age 0.0886 0.2558
#> educ 0.0147 0.0499
#> married 0.0533 0.0533
#> raceblack 0.0154 0.0154
#> racehispan 0.0234 0.0234
#> racewhite 0.0388 0.0388
#> nodegree 0.0097 0.0097
#> re74 0.0611 0.2638
#> re75 0.0644 0.2178
#> log(age) 0.0886 0.2558
#> I(re74 == 0)TRUE 0.2638 0.2638
#>
#> Sample Sizes:
#> Control Treated
#> All 429. 185
#> Matched (ESS) 66.49 185
#> Matched 429. 185
#> Unmatched 0. 0
#> Discarded 0. 0
#>
summary(s.out, subclass = TRUE)
#>
#> Call:
#> matchit(formula = treat ~ age + educ + married + race + nodegree +
#> re74 + re75, data = lalonde, method = "subclass")
#>
#> 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
#> married 0.1892 0.5128 -0.8263 . 0.3236
#> 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
#> 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
#> married 0.3236
#> raceblack 0.6404
#> racehispan 0.0827
#> racewhite 0.5577
#> nodegree 0.1114
#> re74 0.4470
#> re75 0.2876
#>
#> Summary of Balance by Subclass:
#>
#> - Subclass 1
#> Means Treated Means Control Std. Mean Diff. Var. Ratio eCDF Mean
#> distance 0.1376 0.0763 0.7782 1.5087 0.2760
#> age 25.5806 28.4595 -0.4173 0.4163 0.0915
#> educ 10.4839 10.2543 0.1056 0.5625 0.0291
#> married 0.2903 0.5780 -0.6339 . 0.2877
#> raceblack 0.0968 0.0145 0.2784 . 0.0823
#> racehispan 0.3226 0.1734 0.3191 . 0.1492
#> racewhite 0.5806 0.8121 -0.4691 . 0.2315
#> nodegree 0.5806 0.5809 -0.0006 . 0.0003
#> re74 3445.7771 6311.1947 -0.3806 1.1631 0.2055
#> re75 2080.7043 2637.3832 -0.1629 1.0676 0.1057
#> eCDF Max
#> distance 0.4866
#> age 0.1600
#> educ 0.0959
#> married 0.2877
#> raceblack 0.0823
#> racehispan 0.1492
#> racewhite 0.2315
#> nodegree 0.0003
#> re74 0.4293
#> re75 0.2623
#>
#> - Subclass 2
#> Means Treated Means Control Std. Mean Diff. Var. Ratio eCDF Mean
#> distance 0.5081 0.4669 0.6017 1.3761 0.1939
#> age 26.9032 32.2500 -0.8559 0.3404 0.1742
#> educ 9.6129 9.3750 0.0872 0.6596 0.0370
#> married 0.4194 0.6667 -0.5012 . 0.2473
#> raceblack 0.9677 0.9583 0.0533 . 0.0094
#> racehispan 0.0323 0.0417 -0.0533 . 0.0094
#> racewhite 0.0000 0.0000 0.0000 . 0.0000
#> nodegree 0.6129 0.6250 -0.0248 . 0.0121
#> re74 6437.9113 6609.9134 -0.0259 0.9023 0.0549
#> re75 3089.0804 3401.3890 -0.0749 0.9876 0.0819
#> eCDF Max
#> distance 0.3858
#> age 0.4220
#> educ 0.1035
#> married 0.2473
#> raceblack 0.0094
#> racehispan 0.0094
#> racewhite 0.0000
#> nodegree 0.0121
#> re74 0.1882
#> re75 0.2325
#>
#> - Subclass 3
#> Means Treated Means Control Std. Mean Diff. Var. Ratio eCDF Mean
#> distance 0.6262 0.6292 -0.1585 1.0943 0.0541
#> age 25.5517 25.1765 0.0452 0.4654 0.1030
#> educ 10.2759 10.0000 0.1312 0.3845 0.0986
#> married 0.4138 0.0588 0.7207 . 0.3550
#> raceblack 1.0000 1.0000 0.0000 . 0.0000
#> racehispan 0.0000 0.0000 0.0000 . 0.0000
#> racewhite 0.0000 0.0000 0.0000 . 0.0000
#> nodegree 0.6897 0.4706 0.4735 . 0.2191
#> re74 806.7050 2110.7067 -0.5707 0.2635 0.1379
#> re75 628.5508 940.9724 -0.2348 0.6585 0.0723
#> eCDF Max
#> distance 0.1197
#> age 0.2677
#> educ 0.2191
#> married 0.3550
#> raceblack 0.0000
#> racehispan 0.0000
#> racewhite 0.0000
#> nodegree 0.2191
#> re74 0.3225
#> re75 0.1460
#>
#> - Subclass 4
#> Means Treated Means Control Std. Mean Diff. Var. Ratio eCDF Mean
#> distance 0.6823 0.6824 -0.0044 1.4231 0.0509
#> age 23.5000 23.8571 -0.0793 0.2048 0.1491
#> educ 10.1875 10.4762 -0.1570 1.0063 0.0599
#> married 0.0312 0.1429 -0.6414 . 0.1116
#> raceblack 1.0000 1.0000 0.0000 . 0.0000
#> racehispan 0.0000 0.0000 0.0000 . 0.0000
#> racewhite 0.0000 0.0000 0.0000 . 0.0000
#> nodegree 0.6250 0.6667 -0.0861 . 0.0417
#> re74 1099.4532 1134.9752 -0.0121 2.2051 0.0857
#> re75 1446.2640 1704.0458 -0.0568 1.7074 0.0762
#> eCDF Max
#> distance 0.1860
#> age 0.2902
#> educ 0.1711
#> married 0.1116
#> raceblack 0.0000
#> racehispan 0.0000
#> racewhite 0.0000
#> nodegree 0.0417
#> re74 0.3051
#> re75 0.2902
#>
#> - Subclass 5
#> Means Treated Means Control Std. Mean Diff. Var. Ratio eCDF Mean
#> distance 0.7296 0.7346 -0.3468 0.7370 0.1180
#> age 24.0000 22.1111 0.2361 0.7413 0.1059
#> educ 10.2903 10.8889 -0.4814 0.5077 0.0737
#> married 0.0000 0.0000 0.0000 . 0.0000
#> raceblack 1.0000 1.0000 0.0000 . 0.0000
#> racehispan 0.0000 0.0000 0.0000 . 0.0000
#> racewhite 0.0000 0.0000 0.0000 . 0.0000
#> nodegree 0.8710 0.8333 0.1123 . 0.0376
#> re74 525.2020 432.2360 0.0781 2.3025 0.0602
#> re75 754.4831 357.4678 0.2722 4.2576 0.0773
#> eCDF Max
#> distance 0.2832
#> age 0.2885
#> educ 0.1774
#> married 0.0000
#> raceblack 0.0000
#> racehispan 0.0000
#> racewhite 0.0000
#> nodegree 0.0376
#> re74 0.1398
#> re75 0.1613
#>
#> - Subclass 6
#> Means Treated Means Control Std. Mean Diff. Var. Ratio eCDF Mean
#> distance 0.7805 0.7774 0.1490 2.8080 0.1250
#> age 29.4194 25.6667 0.5095 0.4039 0.1945
#> educ 11.2258 10.6667 0.3908 6.1419 0.0707
#> married 0.0000 0.0000 0.0000 . 0.0000
#> raceblack 1.0000 1.0000 0.0000 . 0.0000
#> racehispan 0.0000 0.0000 0.0000 . 0.0000
#> racewhite 0.0000 0.0000 0.0000 . 0.0000
#> nodegree 0.8710 1.0000 -0.3849 . 0.1290
#> re74 207.3736 281.4813 -0.1288 1.3924 0.0878
#> re75 1137.7261 1912.6611 -0.3450 2.6542 0.2079
#> eCDF Max
#> distance 0.3441
#> age 0.6022
#> educ 0.1398
#> married 0.0000
#> raceblack 0.0000
#> racehispan 0.0000
#> racewhite 0.0000
#> nodegree 0.1290
#> re74 0.2043
#> re75 0.7419
#>
#> Sample Sizes by Subclass:
#> 1 2 3 4 5 6 All
#> Control 346 24 17 21 18 3 429
#> Treated 31 31 29 32 31 31 185
#> Total 377 55 46 53 49 34 614
#>