Exact Matching

In matchit(), setting method = "exact" performs exact matching. With exact matching, a complete cross of the covariates is used to form subclasses defined by each combination of the covariate levels. Any subclass that doesn't contain both treated and control units is discarded, leaving only subclasses containing treatment and control units that are exactly equal on the included covariates. The benefits of exact matching are that confounding due to the covariates included is completely eliminated, regardless of the functional form of the treatment or outcome models. The problem is that typically many units will be discarded, sometimes dramatically reducing precision and changing the target population of inference. To use exact matching in combination with another matching method (i.e., to exact match on some covariates and some other form of matching on others), use the exact argument with that method.

This page details the allowable arguments with method = "exact". See 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 exact matching:

matchit(formula,
        data = NULL,
        method = "exact",
        estimand = "ATT",
        s.weights = NULL,
        verbose = FALSE,
        ...)

Arguments

formula

a two-sided formula object containing the treatment and covariates to be used in creating the subclasses defined by a full cross of the covariate levels.

data

a data frame containing the variables named in formula. If not found in data, the variables will be sought in the environment.

method

set here to "exact".

estimand

a string containing the desired estimand. Allowable options include "ATT", "ATC", and "ATE". The estimand controls how the weights are computed; see the Computing Weights section at matchit() for details.

s.weights

the variable containing sampling weights to be incorporated into balance statistics. These weights do not affect the matching process.

verbose

logical; whether information about the matching process should be printed to the console.

...

ignored.

The arguments distance (and related arguments), exact, mahvars, discard (and related arguments), replace, m.order, caliper (and related arguments), and ratio are ignored with a warning.

Outputs

All outputs described in matchit() are returned with method = "exact" except for match.matrix. This is because matching strata are not indexed by treated units as they are in some other forms of matching. include.obj is ignored.

References

In a manuscript, you don't need to cite another package when using method = "exact" because the matching is performed completely within MatchIt. For example, a sentence might read:

Exact matching was performed using the MatchIt package (Ho, Imai, King, & Stuart, 2011) in R.

See also

matchit() for a detailed explanation of the inputs and outputs of a call to matchit(). The exact argument can be used with other methods to perform exact matching in combination with other matching methods.

method_cem for coarsened exact matching, which performs exact matching on coarsened versions of the covariates.

Examples

data("lalonde")

# Exact matching on age, race, married, and educ
m.out1 <- matchit(treat ~ age + race + married + educ, data = lalonde,
                  method = "exact")
m.out1
#> A `matchit` object
#>  - method: Exact matching
#>  - number of obs.: 614 (original), 113 (matched)
#>  - target estimand: ATT
#>  - covariates: age, race, married, educ
summary(m.out1)
#> 
#> Call:
#> matchit(formula = treat ~ age + race + married + educ, data = lalonde, 
#>     method = "exact")
#> 
#> 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
#> 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
#> married           0.1892        0.5128         -0.8263          .    0.3236
#> educ             10.3459       10.2354          0.0550     0.4959    0.0347
#>            eCDF Max
#> age          0.1577
#> raceblack    0.6404
#> racehispan   0.0827
#> racewhite    0.5577
#> married      0.3236
#> educ         0.1114
#> 
#> Summary of Balance for Matched Data:
#>            Means Treated Means Control Std. Mean Diff. Var. Ratio eCDF Mean
#> age              19.9815       19.9815               0     0.9936         0
#> raceblack         0.7778        0.7778               0          .         0
#> racehispan        0.0370        0.0370               0          .         0
#> racewhite         0.1852        0.1852               0          .         0
#> married           0.0370        0.0370               0          .         0
#> educ             10.3333       10.3333               0     0.9936         0
#>            eCDF Max Std. Pair Dist.
#> age               0               0
#> raceblack         0               0
#> racehispan        0               0
#> racewhite         0               0
#> married           0               0
#> educ              0               0
#> 
#> Sample Sizes:
#>               Control Treated
#> All            429.       185
#> Matched (ESS)   40.36      54
#> Matched         59.        54
#> Unmatched      370.       131
#> Discarded        0.         0
#>