" class="reference-link">cobalt: Covariate Balance Tables and Plots

Overview

Welcome to cobalt, which stands for Covariate Balance
Tables (and Plots). cobalt allows users to assess balance on
covariate distributions in preprocessed groups generated through
weighting, matching, or subclassification, such as by using the
propensity score. cobalt’s primary function is bal.tab(), which
stands for “balance table”, and is meant to replace (or supplement) the
balance assessment tools found in other R packages. To examine how
bal.tab() integrates with these packages and others, see the help file
for bal.tab() with ?bal.tab, which links to the methods used for
each package. Each page has examples of how bal.tab() is used with the
package. There are also five vignettes detailing the use of cobalt,
which can be accessed at vignette("cobalt"): one for basic uses of
cobalt, one for the use of cobalt with additional packages, one for
the use of cobalt with multiply imputed and/or clustered data, one for
the use of cobalt with longitudinal treatments, and one for the use of
cobalt to generate publication-ready plots. Currently, cobalt is
compatible with output from MatchIt, twang, Matching, optmatch,
CBPS, ebal, WeightIt, designmatch, sbw, MatchThem, and cem
as well as data not processed through these packages.

For more information, check out the cobalt
website!

Why cobalt?

Most of the major conditioning packages contain functions to assess
balance; so why use cobalt at all? cobalt arose out of several
desiderata when using these packages: to have standardized measures that
were consistent across all conditioning packages, to allow for
flexibility in the calculation and display of balance measures, and to
incorporate recent methodological recommendations in the assessment of
balance. In addition, cobalt has unique plotting capabilities that
make use of ggplot2 in R for balance assessment and reporting.

Because conditioning methods are spread across several packages which
each have their idiosyncrasies in how they report balance (if at all),
comparing the resulting balance from various conditioning methods can be
a challenge. cobalt unites these packages by providing a single,
flexible tool that intelligently processes output from any of the
conditioning packages and provides the user with both useful defaults
and customizable options for display and calculation. cobalt also
allows for balance assessment on data not generated through any of the
conditioning packages. In addition, cobalt has tools for assessing and
reporting balance for clustered data sets, data sets generated through
multiple imputation, and data sets with a continuous treatment variable,
all features that exist in very limited capacities or not at all in
other packages.

A large focus in developing cobalt was to streamline output so that
only the most useful, non-redundant, and complete information is
displayed, all at the user’s choice. Balance statistics are intuitive,
methodologically informed, and simple to interpret. Visual displays of
balance reflect the goals of balance assessment rather than being steps
removed. While other packages have focused their efforts on processing
data, cobalt only assesses balance, and does so particularly well.

New features are being added all the time, following the cutting edge of
methodological work on balance assessment. As new packages and methods
are developed, cobalt will be ready to integrate them to further our
goal of simple, unified balance assessment.

Examples

Below are examples of cobalt’s primary functions:

library("cobalt")
data("lalonde", package = "cobalt")
#Nearest neighbor matching with MatchIt
m.out <- MatchIt::matchit(treat ~ age + educ + race + married +
                              nodegree + re74 + re75,
                          data = lalonde)
#Checking balance before and after matching:
bal.tab(m.out, thresholds = c(m = .1), un = TRUE)

#> Balance Measures
#>                 Type Diff.Un Diff.Adj        M.Threshold
#> distance    Distance  1.7941   0.9739                   
#> age          Contin. -0.3094   0.0718     Balanced, <0.1
#> educ         Contin.  0.0550  -0.1290 Not Balanced, >0.1
#> race_black    Binary  0.6404   0.3730 Not Balanced, >0.1
#> race_hispan   Binary -0.0827  -0.1568 Not Balanced, >0.1
#> race_white    Binary -0.5577  -0.2162 Not Balanced, >0.1
#> married       Binary -0.3236  -0.0216     Balanced, <0.1
#> nodegree      Binary  0.1114   0.0703     Balanced, <0.1
#> re74         Contin. -0.7211  -0.0505     Balanced, <0.1
#> re75         Contin. -0.2903  -0.0257     Balanced, <0.1
#> 
#> Balance tally for mean differences
#>                    count
#> Balanced, <0.1         5
#> Not Balanced, >0.1     4
#> 
#> Variable with the greatest mean difference
#>    Variable Diff.Adj        M.Threshold
#>  race_black    0.373 Not Balanced, >0.1
#> 
#> Sample sizes
#>           Control Treated
#> All           429     185
#> Matched       185     185
#> Unmatched     244       0

#Examining distributional balance with plots:
bal.plot(m.out, var.name = "educ")
bal.plot(m.out, var.name = "distance",
         mirror = TRUE, type = "histogram")

#Generating a Love plot to report balance:
love.plot(m.out, stats = c("mean.diffs", "variance.ratios"),
          thresholds = c(m = .1, v = 2), abs = TRUE, 
          binary = "std",
          var.order = "unadjusted")

Please remember to cite this package when using it to analyze data. For
example, in a manuscript, you could write: “Matching was performed using
the Matching package (Sekhon, 2011), and covariate balance was
assessed using cobalt (Greifer, 2024), both in R (R Core Team, 2023).”
Use citation("cobalt") to generate a bibliographic reference for the
cobalt package.

Bugs appear in cobalt occasionally, often found by users. Please
report any bugs at https://github.com/ngreifer/cobalt/issues. To
install the latest development version of cobalt, which may have
removed a bug you’re experiencing, use the following code:

devtools::install_github("ngreifer/cobalt")