cobalt

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 essentially repalces (or supplements) the balance assessment tools found in the R packages twang, MatchIt, CBPS, and Matching. 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 two vignette detailing the use of cobalt, which can be accessed with browseVignettes("cobalt"): one for basic uses of cobalt, and the other for the use of cobalt with multiply imputed and/or clustered data. Currently, cobalt is compatible with output from MatchIt, twang, Matching, optmatch, CBPS, and ebal, as well as data not processed through these packages.

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 devloping 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, methodological 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 methodolgocial 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.

Below are examples of cobalt’s primary functions:

library("cobalt")
library("MatchIt")
data("lalonde", package = "cobalt")

#Nearest neighbor matching with MatchIt
m.out <- matchit(treat ~ age + educ + race + married + nodegree +
                     re74 + re75, data = lalonde)

#Checking balance before and after matching:
bal.tab(m.out, m.threshold = .1, un = TRUE)
#> 
#> Call:
#> matchit(formula = treat ~ age + educ + race + married + nodegree + 
#>     re74 + re75, data = lalonde)
#> 
#> 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:
#>            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 = "race")

#Generating a Love plot to report balance:
love.plot(bal.tab(m.out), threshold = .1, abs = TRUE, var.order = "unadjusted")

Please remember to cite this package when using it to analyze data. For example, in a manuscript, write: “Matching was performed using Matching (Sekhon, 2011), and covariate balance was assessed using cobalt (Greifer, 2017) in R (R Core team, 2017).” Use citation("cobalt") to generate a bibliographic reference for the cobalt package.