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.

# S3 method for matchit
summary(object, interactions = FALSE,
        addlvariables = NULL, standardize = TRUE,
        data = NULL, pair.dist = TRUE, un = TRUE,
        improvement = TRUE, ...)
# S3 method for matchit.subclass
summary(object, interactions = FALSE,
        addlvariables = NULL, standardize = TRUE,
        data = NULL, pair.dist = FALSE, subclass = FALSE,
        un = TRUE, improvement = TRUE, ...)

# S3 method for summary.matchit
print(x, digits = max(3, getOption("digits") - 3),
      ...)
# S3 method for summary.matchit.subclass
print(x, digits = max(3, getOption("digits") - 3),
      ...)

Arguments

object

a matchit object; the output of a call to matchit().

interactions

logical; whether to compute balance statistics for two-way interactions and squares of covariates. Default is FALSE.

addlvariables

additional variable for which balance statistics are to be computed along with the covariates in the matchit object. Can be entered in one of three ways: as a data frame of covariates with as many rows as there were units in the original matchit() call, as a string containing the names of variables in data, or as a right-sided formula with the additional variables (and possibly their transformations) found in data, the environment, or the matchit object. Balance on squares and interactions of the additional variables will be included if interactions = TRUE.

standardize

logical; whether to compute standardized (TRUE) or unstandardized (FALSE) statistics. The standardized statistics are the standardized mean difference and the median, mean, and maximum of the difference in the (weighted) empirical cumulative distribution functions (ECDFs). The unstandardized statistics are the raw mean difference and the median, mean, and maximum of the quantile-quantile (QQ) difference. See Details below. Default is TRUE.

data

a optional data frame containing variables named in addlvariables if specified as a string or formula.

pair.dist

logical; whether to compute average absolute pair distances. For matching methods that don't include a match.matrix component 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 is FALSE for subclassification and TRUE otherwise.

un

logical; whether to compute balance statistics for the unmatched sample. Default TRUE; set to FALSE for more concise output.

improvement

logical; whether to compute the percent reduction in imbalance. Default TRUE; set to FALSE for more concise output.

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 than FALSE, aggregate balance statistics will not be displayed. Default is FALSE.

digits

the number of digits to round balance statistics to.

x

a summay.matchit or summary.matchit.subclass object; the output of a call to summary().

...

ignored.

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 not "mahalanobis". 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.

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 subclass is not FALSE, 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

See also

summary() for the generic method; plot.summary.matchit() for making a Love plot from summary() output.

cobalt::bal.tab(), 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                0.3762   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     0.9763
#> age                        25.8162       25.9568         -0.0196     0.4497
#> educ                       10.3459       10.6054         -0.1290     0.5294
#> 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     1.4993
#> age²                      717.3946      785.1351         -0.1571     0.3691
#> age * educ                266.9784      268.1514         -0.0127     0.6106
#> age * married               5.5568        5.3459          0.0176     1.0537
#> age * raceblack            21.9081       21.1243          0.0676     0.7597
#> age * racehispan            1.3568        1.7297         -0.0677     0.5425
#> age * racewhite             2.5514        3.1027         -0.0682     0.6514
#> age * re74              54074.0365    56189.4636         -0.0160     0.9090
#> educ²                     111.0595      119.9459         -0.2261     0.5334
#> educ * married              1.9622        2.0432         -0.0194     0.9008
#> educ * raceblack            8.6973        8.7730         -0.0180     0.7950
#> educ * racehispan           0.5784        0.6054         -0.0116     0.8758
#> educ * racewhite            1.0703        1.2270         -0.0473     0.8110
#> educ * re74             22898.7264    21438.6573          0.0254     1.4247
#> 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     2.2352
#> raceblack * re74         1817.2003     1629.7393          0.0394     1.4577
#> racehispan * re74         151.3968       90.2110          0.0511     3.1358
#> racewhite * re74          126.9766      162.2167         -0.0381     1.0618
#> re74²                28141411.5686 19125662.0900          0.0790     2.9765
#>                      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
#> 
#> Percent Balance Improvement:
#>                      Std. Mean Diff. Var. Ratio eCDF Mean eCDF Max
#> distance                        99.9       81.9      99.2     93.3
#> age                             93.7        2.7       6.0    -40.5
#> educ                          -134.8        9.3      -1.6    -69.9
#> married                        100.0          .     100.0    100.0
#> raceblack                       98.3          .      98.3     98.3
#> racehispan                     100.0          .     100.0    100.0
#> racewhite                       98.1          .      98.1     98.1
#> re74                            93.9       38.4      75.8     34.7
#> age²                            63.3        1.7       6.0    -40.5
#> age * educ                      92.4       30.6      40.0     18.1
#> age * married                   98.1       93.2      94.6     80.0
#> age * raceblack                 95.3    -4914.9      66.6     70.2
#> age * racehispan                84.5       47.4      71.7     67.3
#> age * racewhite                 96.6       69.8      93.3     95.2
#> age * re74                      98.4       93.0      76.4     34.7
#> educ²                         -383.5        4.7      -1.6    -69.9
#> educ * married                  97.4       80.0      95.3     74.4
#> educ * raceblack                98.9    -1038.7      90.5     75.7
#> educ * racehispan               96.1       81.6      89.7     72.3
#> educ * racewhite                97.3       79.2      97.2     96.1
#> educ * re74                     96.1       46.1      76.8     33.7
#> married * raceblack             94.5          .      94.5     94.5
#> married * racehispan            89.5          .      89.5     89.5
#> married * racewhite            100.0          .     100.0    100.0
#> married * re74                  96.2       34.7      93.2     76.1
#> raceblack * re74                84.2       67.3      47.9    -63.2
#> racehispan * re74               88.4       32.4      86.4     79.6
#> racewhite * re74                99.2       98.5      97.3     92.4
#> re74²                           81.8     -157.6      75.8     34.7
#> 
#> Sample Sizes:
#>               Control Treated
#> All            429.       185
#> Matched (ESS)   46.31     185
#> Matched         82.       185
#> Unmatched      347.         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
#> 
#> Percent Balance Improvement:
#>                  Std. Mean Diff. Var. Ratio eCDF Mean eCDF Max
#> distance                    95.9       11.2      90.3     87.3
#> age                         80.3        0.6      -9.0    -62.2
#> educ                        41.2      -28.9      57.7     55.2
#> married                     83.5          .      83.5     83.5
#> raceblack                   97.6          .      97.6     97.6
#> racehispan                  71.7          .      71.7     71.7
#> racewhite                   93.0          .      93.0     93.0
#> nodegree                    91.3          .      91.3     91.3
#> re74                        79.7      -70.0      72.8     41.0
#> re75                        67.6      -27.9      52.0     24.3
#> log(age)                    57.7        4.1      -9.0    -62.2
#> I(re74 == 0)TRUE            41.0          .      41.0     41.0
#> 
#> 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          0.2785     1.5087    0.2760
#> age              25.5806       28.4595         -0.4024     0.4163    0.0915
#> educ             10.4839       10.2543          0.1142     0.5625    0.0291
#> married           0.2903        0.5780         -0.7346          .    0.2877
#> raceblack         0.0968        0.0145          0.2264          .    0.0823
#> racehispan        0.3226        0.1734          0.6308          .    0.1492
#> racewhite         0.5806        0.8121         -0.7811          .    0.2315
#> nodegree          0.5806        0.5809         -0.0006          .    0.0003
#> re74           3445.7771     6311.1947         -0.5864     1.1631    0.2055
#> re75           2080.7043     2637.3832         -0.1729     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          0.1873     1.3761    0.1939
#> age              26.9032       32.2500         -0.7473     0.3404    0.1742
#> educ              9.6129        9.3750          0.1183     0.6596    0.0370
#> married           0.4194        0.6667         -0.6314          .    0.2473
#> raceblack         0.9677        0.9583          0.0259          .    0.0094
#> racehispan        0.0323        0.0417         -0.0398          .    0.0094
#> racewhite         0.0000        0.0000          0.0000          .    0.0000
#> nodegree          0.6129        0.6250         -0.0266          .    0.0121
#> re74           6437.9113     6609.9134         -0.0352     0.9023    0.0549
#> re75           3089.0804     3401.3890         -0.0970     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         -0.0140     1.0943    0.0541
#> age              25.5517       25.1765          0.0524     0.4654    0.1030
#> educ             10.2759       10.0000          0.1372     0.3845    0.0986
#> married           0.4138        0.0588          0.9063          .    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          0.4819          .    0.2191
#> re74            806.7050     2110.7067         -0.2669     0.2635    0.1379
#> re75            628.5508      940.9724         -0.0970     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         -0.0003     1.4231    0.0509
#> age              23.5000       23.8571         -0.0499     0.2048    0.1491
#> educ             10.1875       10.4762         -0.1436     1.0063    0.0599
#> married           0.0312        0.1429         -0.2850          .    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         -0.0916          .    0.0417
#> re74           1099.4532     1134.9752         -0.0073     2.2051    0.0857
#> re75           1446.2640     1704.0458         -0.0801     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         -0.0224     0.7370    0.1180
#> age              24.0000       22.1111          0.2640     0.7413    0.1059
#> educ             10.2903       10.8889         -0.2977     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          0.0828          .    0.0376
#> re74            525.2020      432.2360          0.0190     2.3025    0.0602
#> re75            754.4831      357.4678          0.1233     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          0.0143     2.8080    0.1250
#> age              29.4194       25.6667          0.5245     0.4039    0.1945
#> educ             11.2258       10.6667          0.2781     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         -0.2838          .    0.1290
#> re74            207.3736      281.4813         -0.0152     1.3924    0.0878
#> re75           1137.7261     1912.6611         -0.2407     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
#>