cobalt with Other
Preprocessing PackagesThis is an appendix to the main
vignette, “Covariate Balance Tables and Plots: A Guide to the cobalt
Package”. It contains descriptions and demonstrations of several utility
functions in cobalt and the use of bal.tab()
with twang, Matching, optmatch,
CBPS, ebal, designmatch,
sbw, MatchThem, and cem. Note
that MatchIt can perform most of the functions that
Matching, optmatch, and cem can,
and WeightIt can perform most of the functions that
twang, CBPS, ebal, and
sbw can. Because cobalt has been optimized to
work with MatchIt and WeightIt, it is
recommended to use those packages to simplify preprocessing and balance
assessment, but we recognize users may prefer to use the packages
described in this vignette.
In addition to its main balance assessment functions,
cobalt contains several utility functions. These are meant
to reduce the typing and programming burden that often accompany the use
of R with a diverse set of packages.
splitfactor() and unsplitfactor()Some functions (outside of cobalt) are not friendly to
factor or character variables, and require numeric variables to operate
correctly. For example, some regression-style functions, such as
ebalance() in ebal, can only take in
non-singular numeric matrices. Other functions will process factor
variables, but will return output in terms of dummy coded version of the
factors. For example, lm() will create dummy variables out
of a factor and drop the reference category to create regression
coefficients.
To prepare data sets for use in functions that do not allow factors
or to mimic the output of functions that split factor variables, users
can use splitfactor(), which takes in a data set and the
names of variables to split, and outputs a new data set with newly
created dummy variables. Below is an example splitting the
race variable in the Lalonde data set into dummies,
eliminating the reference category ("black"):
## treat age educ race married nodegree re74 re75 re78
## 1 1 37 11 black 1 1 0 0 9930.0460
## 2 1 22 9 hispan 0 1 0 0 3595.8940
## 3 1 30 12 black 0 0 0 0 24909.4500
## 4 1 27 11 black 0 1 0 0 7506.1460
## 5 1 33 8 black 0 1 0 0 289.7899
## 6 1 22 9 black 0 1 0 0 4056.4940## treat age educ race_hispan race_white married nodegree re74 re75 re78
## 1 1 37 11 0 0 1 1 0 0 9930.0460
## 2 1 22 9 1 0 0 1 0 0 3595.8940
## 3 1 30 12 0 0 0 0 0 0 24909.4500
## 4 1 27 11 0 0 0 1 0 0 7506.1460
## 5 1 33 8 0 0 0 1 0 0 289.7899
## 6 1 22 9 0 0 0 1 0 0 4056.4940It is possible to undo the action of splitfactor() with
unsplitfactor(), which takes in a data set with dummy
variables formed from splitfactor() or otherwise and
recreates the original factor variable. If the reference category was
dropped, its value needs to be supplied.
lalonde.unsplit <- unsplitfactor(lalonde.split, "race",
dropped.level = "black")
head(lalonde.unsplit)## treat age educ race married nodegree re74 re75 re78
## 1 1 37 11 black 1 1 0 0 9930.0460
## 2 1 22 9 hispan 0 1 0 0 3595.8940
## 3 1 30 12 black 0 0 0 0 24909.4500
## 4 1 27 11 black 0 1 0 0 7506.1460
## 5 1 33 8 black 0 1 0 0 289.7899
## 6 1 22 9 black 0 1 0 0 4056.4940Notice the original data set and the unsplit data set look identical.
If the input to unsplitfactor() is the output of a call to
splitfactor() (as it was here), you don’t need to tell
unsplitfactor() the name of the split variable or the value
of the dropped level. It was done here for illustration purposes.
get.w()get.w() allows users to extract weights from the output
of a call to a preprocessing function in one of the supported packages.
Because each package stores weights in different ways, it can be helpful
to have a single function that applies equally to all outputs.
twang has a function called get.weights() that
performs the same functions with slightly finer control for the output
of a call to ps().
bal.tab()The next sections describe the use of bal.tab() with
packages other than those described in the main vignette. Even if you
are using bal.tab() with one of these packages, it may be
useful to read the main vignette to understand bal.tab()’s
main options, which are not detailed here.
bal.tab() with twangGeneralized boosted modeling (GBM), as implemented in
twang, can be an effective way to generate propensity
scores and weights for use in propensity score weighting.
bal.tab() functions similarly to the functions
bal.table() and summary() when used with GBM
in twang. Below is a simple example of its use:
#GBM PS weighting for the ATT
data("lalonde", package = "cobalt") ##If not yet loaded
covs0 <- subset(lalonde, select = -c(treat, re78))
f <- reformulate(names(covs0), "treat")
ps.out <- twang::ps(f, data = lalonde,
stop.method = c("es.mean", "es.max"),
estimand = "ATT", n.trees = 1000,
verbose = FALSE)
bal.tab(ps.out, stop.method = "es.mean")## Balance Measures
## Type Diff.Adj
## prop.score Distance 0.5189
## age Contin. 0.0400
## educ Contin. -0.0819
## race_black Binary 0.0687
## race_hispan Binary -0.0032
## race_white Binary -0.0817
## married Binary -0.0296
## nodegree Binary 0.1901
## re74 Contin. 0.0691
## re75 Contin. 0.0953
##
## Effective sample sizes
## Control Treated
## Unadjusted 429. 185
## Adjusted 33.03 185The output looks a bit different from twang’s
bal.table() output. First is the original call to
ps(). Next is the balance table containing mean differences
for the covariates included in the input to ps(). Last is a
table displaying sample size information, similar to what would be
generated using twang’s summary() function.
The “effective” sample size is displayed when weighting is used; it is
calculated as is done in twang. See the twang
documentation, ?bal.tab, or “Details on Calculations” in
the main vignette for details on this calculation.
When using bal.tab() with twang, the user
must specify the ps object, the output of a call to
ps(), as the first argument. The second argument,
stop.method, is the name of the stop method(s) for which
balance is to be assessed, since a ps object may contain
more than one if so specified. bal.tab() can display the
balance for more than one stop method at a time by specifying a vector
of stop method names. If this argument is left empty or if the argument
to stop.method does not correspond to any of the stop
methods in the ps object, bal.tab() will
default to displaying balance for all stop methods available.
Abbreviations are allowed for the stop method, which is not case
sensitive.
The other arguments to bal.tab() when using it with
twang have the same form and function as those given when
using it without a conditioning package, except for
s.d.denom. If the estimand of the stop method used is the
ATT, s.d.denom will default to "treated" if
not specified, and if the estimand is the ATE, s.d.denom
will default to "pooled", mimicking the behavior of
twang. The user can specify their own argument to
s.d.denom, but using the defaults is advised.
If sampling weights are used in the call to ps(), they
will be automatically incorporated into the bal.tab()
calculations for both the adjusted and unadjusted samples, just as
twang does.
mnps objects resulting from fitting models in
twang with multi-category treatments are also compatible
with cobalt. See the section “Using cobalt
with multi-category treatments” in the main vignette. iptw
objects resulting from fitting models in twang with
longitudinal treatments are also compatible with cobalt.
See the Appendix 3 vignette. ps.cont objects resulting from
using ps.cont() in twangContinuous, which
implements GBM for continuous treatments, are also compatible. See the
section “Using cobalt with continuous treatments” in the
main vignette.
bal.tab() with MatchingThe Matching package is used for propensity score
matching, and was also the first package to implement genetic matching.
MatchIt calls Matching to use genetic matching
and can accomplish many of the matching methods Matching
can, but Matching is still a widely used package with its
own strengths. bal.tab() functions similarly to
Matching’s MatchBalance() command, which
yields a thorough presentation of balance. Below is a simple example of
the use of bal.tab() with Matching:
#1:1 NN PS matching w/ replacement
data("lalonde", package = "cobalt") #If not yet loaded
covs0 <- subset(lalonde, select = -c(treat, re78))
f <- reformulate(names(covs0), "treat")
fit <- glm(f, data = lalonde, family = binomial)
p.score <- fit$fitted.values
match.out <- Matching::Match(Tr = lalonde$treat, X = p.score,
estimand = "ATT")
bal.tab(match.out, formula = f, data = lalonde,
distance = ~ p.score)## Balance Measures
## Type Diff.Adj
## p.score Distance 0.0043
## age Contin. 0.2106
## educ Contin. 0.0201
## race_black Binary 0.0149
## race_hispan Binary -0.0217
## race_white Binary -0.0009
## married Binary 0.1688
## nodegree Binary -0.0173
## re74 Contin. -0.0772
## re75 Contin. -0.0127
##
## Sample sizes
## Control Treated
## All 429. 185
## Matched (ESS) 49.17 185
## Matched (Unweighted) 136. 185
## Unmatched 293. 0The output looks quite different from Matching’s
MatchBalance() output. Rather than being stacked
vertically, balance statistics are arranged horizontally in a table
format, allowing for quick balance checking. Below the balance table is
a summary of the sample size before and after matching, similar to what
Matching’s summary() command would display.
The sample size can include an “ESS” and “unweighted” value; the “ESS”
value is the effective sample size resulting from the matching weights,
while the “unweighted” is the count of units with nonzero matching
weights.
The input to bal.tab() is similar to that given to
MatchBalance(): the Match object resulting
from the call to Match(), a formula relating treatment to
the covariates for which balance is to be assessed, and the original
data set. This is not the only way to call bal.tab():
instead of a formula and a data set, one can also input a data frame of
covariates and a vector of treatment status indicators, just as when
using bal.tab() without a conditioning package. For
example, the code below will yield the same results as the call to
bal.tab() above:
The other arguments to bal.tab() when using it with
Matching have the same form and function as those given
when using it without a conditioning package, except for
s.d.denom. If the estimand of the original call to
Match() is the ATT, s.d.denom will default to
"treated" if not specified; if the estimand is the ATE,
s.d.denom will default to "pooled"; if the
estimand is the ATC, s.d.denom will default to
"control". The user can specify their own argument to
s.d.denom, but using the defaults is advisable. In
addition, the use of the addl argument is unnecessary
because the covariates are entered manually as arguments, so all
covariates for which balance is to be assessed can be entered through
the formula or covs argument. If the
covariates are stored in two separate data frames, it may be useful to
include one in formula or covs and the other
in addl.
bal.tab() with optmatchThe optmatch package is useful for performing optimal
pairwise or full matching. Most functions in optmatch are
subsumed in MatchIt, but optmatch sees use
from those who want finer control of the matching process than
MatchIt allows. The output of calls to functions in
optmatch is an optmatch object, which contains
matching stratum membership for each unit in the given data set. Units
that are matched with each other are assigned the same matching stratum.
The user guide for optmatch recommends using the
RItools package for balance assessment, but below is an
example of how to use bal.tab() for the same purpose. Note
that some results will differ between cobalt and
RItools because of differences in how balance is calculated
in each.
#Optimal full matching on the propensity score
data("lalonde", package = "cobalt") #If not yet loaded
covs0 <- subset(lalonde, select = -c(treat, re78))
f <- reformulate(names(covs0), "treat")
fit <- glm(f, data = lalonde, family = binomial)
p.score <- fit$fitted.values #get the propensity score
fm <- optmatch::fullmatch(treat ~ p.score, data = lalonde)
bal.tab(fm, covs = covs0, distance = ~ p.score)## Balance Measures
## Type Diff.Adj
## p.score Distance 0.0053
## age Contin. 0.1479
## educ Contin. -0.0158
## race_black Binary 0.0236
## race_hispan Binary -0.0262
## race_white Binary -0.0081
## married Binary 0.1463
## nodegree Binary 0.0082
## re74 Contin. -0.0696
## re75 Contin. -0.0175
##
## Sample sizes
## Control Treated
## All 429. 185
## Matched (ESS) 51.53 185
## Matched (Unweighted) 429. 185Most details for the use of bal.tab() with
optmatch are similar to those when using
bal.tab() with Matching. Users can enter
either a formula and a data set or a vector of treatment status and a
set of covariates. Unlike with Matching, entering the
treatment variable is optional as it is already stored in the
optmatch object. bal.tab() is compatible with
both pairmatch() and fullmatch() output.
bal.tab() with CBPSThe CBPS (Covariate Balancing Propensity Score) package
is a great tool for generating covariate balancing propensity scores, a
class of propensity scores that are quite effective at balancing
covariates among groups. CBPS includes functions for
estimating propensity scores for binary, multi-category, and continuous
treatments. bal.tab() functions similarly to
CBPS’s balance() command. Below is a simple
example of its use with a binary treatment:
#CBPS weighting
data("lalonde", package = "cobalt") #If not yet loaded
covs0 <- subset(lalonde, select = -c(treat, re78))
f <- reformulate(names(covs0), "treat")
#Generating covariate balancing propensity score weights for ATT
cbps.out <- CBPS::CBPS(f, data = lalonde)## [1] "Finding ATT with T=1 as the treatment. Set ATT=2 to find ATT with T=0 as the treatment"## Balance Measures
## Type Diff.Adj
## prop.score Distance -0.0057
## age Contin. -0.0052
## educ Contin. -0.0017
## race_black Binary 0.0053
## race_hispan Binary -0.0009
## race_white Binary -0.0057
## married Binary -0.0073
## nodegree Binary 0.0092
## re74 Contin. -0.0078
## re75 Contin. 0.0061
##
## Effective sample sizes
## Control Treated
## Unadjusted 429. 185
## Adjusted 99.97 185First is the original call to CBPS(). Next is the
balance table containing mean differences for the covariates included in
the input to CBPS(). Last is a table displaying sample size
information. The “effective” sample size is displayed when weighting
(rather than matching or subclassification) is used; it is calculated as
is done in twang. See the twang documentation,
?bal.tab, or “Details on Calculations” in the main vignette
for details on this calculation.
The other arguments to bal.tab() when using it with
CBPS have the same form and function as those given when
using it without a conditioning package, except for
s.d.denom. If the estimand of the original call to
CBPS() is the ATT, s.d.denom will default to
"treated" if not specified, and if the estimand is the ATE,
s.d.denom will default to "pooled". The user
can specify their own argument to s.d.denom, but using the
defaults is advisable.
CBPSContinuous objects resulting from fitting models in
CBPS with continuous treatments are also compatible with
cobalt. See the section “Using cobalt with
continuous treatments” in the main vignette. CBPS objects
resulting from fitting models in CBPS with multi-category
treatments are also compatible with cobalt. See the section
“Using cobalt with multi-category treatments” in the main
vignette. CBMSM objects resulting from fitting models in
CBPS with longitudinal treatments are also compatible with
cobalt. See the Appendix 3 vignette.
bal.tab() with ebalThe ebal package implements entropy balancing, a method
of weighting for the ATT that yields perfect balance on all desired
moments of the covariate distributions between groups. Rather than
estimate a propensity score, entropy balancing generates weights
directly that satisfy a user-defined moment condition, specifying which
moments are to be balanced. Note that all the functionality of
ebal is contained within Weightit.
ebal does not have its own balance assessment function;
thus, cobalt is the only way to assess balance without
programming, which the ebal documentation instructs. Below
is a simple example of using bal.tab() with
ebal:
#Entropy balancing
data("lalonde", package = "cobalt") #If not yet loaded
covs0 <- subset(lalonde, select = -c(treat, re78, race))
#Generating entropy balancing weights
e.out <- ebal::ebalance(lalonde$treat, covs0)## Converged within tolerance## Balance Measures
## Type Diff.Adj
## age Contin. -0
## educ Contin. -0
## married Binary -0
## nodegree Binary 0
## re74 Contin. -0
## re75 Contin. -0
##
## Effective sample sizes
## Control Treated
## Unadjusted 429. 185
## Adjusted 247.64 185First is the balance table containing mean differences for covariates
included in the original call to ebalance. In general,
these will all be very close to 0. Next is a table displaying effective
sample size information. See ?bal.tab or “Details on
Calculations” in the main vignette for details on this calculation. A
common issue when using entropy balancing is small effective sample
size, which can yield low precision in effect estimation when using
weighted regression, so it is important that users pay attention to this
measure.
The input is similar to that for using bal.tab() with
Matching or optmatch. In addition to the
ebalance object, one must specify either both a formula and
a data set or both a treatment vector and a data frame of
covariates.
bal.tab() with designmatchThe designmatch package implements various matching
methods that use optimization to find matches that satisfy certain
balance constraints. bal.tab() functions similarly to
designmatch’s meantab() command but provides
additional flexibility and convenience. Below is a simple example of
using bal.tab() with designmatch:
#Mixed integer programming matching
library("designmatch")
data("lalonde", package = "cobalt") #If not yet loaded
covs0 <- subset(lalonde, select = -c(treat, re78, race))
#Matching for balance on covariates
dmout <- bmatch(lalonde$treat,
dist_mat = NULL,
subset_weight = NULL,
mom = list(covs = covs0,
tols = absstddif(covs0, lalonde$treat, .05)),
n_controls = 1,
total_groups = 185)## Building the matching problem...
## HiGHS optimizer is open...
## Finding the optimal matches...
## Optimal matches found
## HiGHS optimizer is open...
## Finding the optimal matches...
## Optimal matches found## Balance Measures
## Type Diff.Adj
## age Contin. -0.0733
## educ Contin. 0.0618
## married Binary -0.0690
## nodegree Binary 0.0594
## re74 Contin. -0.0623
## re75 Contin. 0.0472
##
## Sample sizes
## Control Treated
## All 429 185
## Matched 185 185
## Unmatched 244 0The input is similar to that for using bal.tab() with
Matching or optmatch. In addition to the
designmatch() output object, one must specify either both a
formula and a data set or both a treatment vector and a data frame of
covariates. The output is similar to that of optmatch.
bal.tab() with sbwThe sbw package implements optimization-based weighting
to estimate weights that satisfy certain balance constraints and have
minimal variance. bal.tab() functions similarly to
sbw’s summarize() function but provides
additional flexibility and convenience. Below is a simple example of
using bal.tab() with sbw:
#Optimization-based weighting
data("lalonde", package = "cobalt") #If not yet loaded
lalonde_split <- splitfactor(lalonde, drop.first = "if2")
cov.names <- setdiff(names(lalonde_split), c("treat", "re78"))
#Estimating balancing weights for the ATT
sbw.out <- sbw::sbw(lalonde_split,
ind = "treat",
bal = list(bal_cov = cov.names,
bal_alg = FALSE,
bal_tol = .001),
par = list(par_est = "att"))## Building the weighting problem...
## quadprog optimizer is open...
## Finding the optimal weights...
## Optimal weights found.## Balance Measures
## Type M.0.Un M.1.Un Diff.Un M.0.Adj M.1.Adj Diff.Adj
## age Contin. 28.0303 25.8162 -0.3094 25.8054 25.8162 0.0015
## educ Contin. 10.2354 10.3459 0.0550 10.3431 10.3459 0.0014
## race_black Binary 0.2028 0.8432 1.7615 0.8428 0.8432 0.0011
## race_hispan Binary 0.1422 0.0595 -0.3498 0.0594 0.0595 0.0003
## race_white Binary 0.6550 0.0973 -1.8819 0.0978 0.0973 -0.0016
## married Binary 0.5128 0.1892 -0.8263 0.1897 0.1892 -0.0013
## nodegree Binary 0.5967 0.7081 0.2450 0.7076 0.7081 0.0011
## re74 Contin. 5619.2365 2095.5737 -0.7211 2102.3624 2095.5737 -0.0014
## re75 Contin. 2466.4844 1532.0553 -0.2903 1528.7633 1532.0553 0.0010
##
## Effective sample sizes
## Control Treated
## Unadjusted 429. 185
## Adjusted 108.99 185The output is similar to the output of a call to
summarize(). Rather than stack several balance tables
vertically, each with their own balance summary, here they are displayed
horizontally. Note that due to differences in how sbw and
cobalt compute the standardization factor in the
standardized mean difference, values may not be identical between
bal.tab() and summarize(). Also note that
bal.tab()’s default is to display raw rather than
standardized mean differences for binary variables.
bal.tab() with MatchThemThe MatchThem package is essentially a wrapper for
matchit() from MatchIt and
weightit() from WeightIt but for use with
multiply imputed data. Using bal.tab() on
mimids or wimids objects from
MatchThem activates the features that accompany multiply
imputed data; balance is assessed within each imputed dataset and
aggregated across imputations. See ?bal.tab.imp or
vignette("segmented-data") for more information about using
cobalt with multiply imputed data. Below is a simple
example of using bal.tab() with MatchThem:
#PS weighting on multiply imputed data
data("lalonde_mis", package = "cobalt")
#Generate imputed data sets
m <- 10 #number of imputed data sets
imp.out <- mice::mice(lalonde_mis, m = m, print = FALSE)
#Matching for balance on covariates
mt.out <- MatchThem::matchthem(treat ~ age + educ + married +
race + re74 + re75,
datasets = imp.out,
approach = "within",
method = "nearest",
estimand = "ATT")
bal.tab(mt.out)## Balance summary across all imputations
## Type Min.Diff.Adj Mean.Diff.Adj Max.Diff.Adj
## distance Distance 0.9505 0.9558 0.9621
## age Contin. -0.0567 0.0175 0.0695
## educ Contin. -0.1801 -0.1441 -0.0645
## married Binary -0.0683 -0.0420 -0.0135
## race_black Binary 1.0259 1.0259 1.0259
## race_hispan Binary -0.8000 -0.7543 -0.7086
## race_white Binary -0.6931 -0.6566 -0.6201
## re74 Contin. -0.0768 -0.0546 -0.0190
## re75 Contin. -0.0964 -0.0582 -0.0385
##
## Average sample sizes across imputations
## 0 1
## All 429 185
## Matched 185 185
## Unmatched 244 0#Weighting for balance on covariates
wt.out <- MatchThem::weightthem(treat ~ age + educ + married +
race + re74 + re75,
datasets = imp.out,
approach = "within",
method = "glm",
estimand = "ATE")
bal.tab(wt.out)## Balance summary across all imputations
## Type Min.Diff.Adj Mean.Diff.Adj Max.Diff.Adj
## prop.score Distance 0.1358 0.1549 0.1650
## age Contin. -0.1954 -0.1877 -0.1828
## educ Contin. 0.0753 0.0847 0.0913
## married Binary -0.2497 -0.2203 -0.1891
## race_black Binary 0.1242 0.1467 0.1625
## race_hispan Binary 0.0183 0.0308 0.0442
## race_white Binary -0.1845 -0.1652 -0.1365
## re74 Contin. -0.2970 -0.2868 -0.2561
## re75 Contin. -0.1626 -0.1568 -0.1475
##
## Average effective sample sizes across imputations
## 0 1
## Unadjusted 429. 185.
## Adjusted 331.71 66.61The input is similar to that for using bal.tab() with
MatchIt or WeightIt.
bal.tab() with cemThe cem package implements coarsened exact matching for
binary and multi-category treatments. bal.tab() functions
similarly to cems’s imbalance(). Below is a
simple example of using bal.tab() with
cem:
#Coarsened exact matching
data("lalonde", package = "cobalt") #If not yet loaded
#Matching for balance on covariates
cem.out <- cem::cem("treat", data = lalonde, drop = "re78")
bal.tab(cem.out, data = lalonde, stats = c("m", "ks"))The input is similar to that for using bal.tab() with
Matching or optmatch. In addition to the
cem() output object, one must specify either both a formula
and a data set or both a treatment vector and a data frame of
covariates. Unlike with Matching, entering the treatment
variable is optional as it is already stored in the output object. The
output is similar to that of optmatch.
When using cem() with multiply imputed data (i.e., by
supplying a list of data.frames to the datalist argument in
cem()), an argument to imp should be specified
to bal.tab() or a mids object from the
mice package should be given as the argument to
data. See ?bal.tab.imp or
vignette("segmented-data") for more information about using
cobalt with multiply imputed data. Below is an example of
using cem with multiply imputed data from
mice:
#Coarsened exact matching on multiply imputed data
data("lalonde_mis", package = "cobalt")
#Generate imputed data sets
m <- 10 #number of imputed data sets
imp.out <- mice::mice(lalonde_mis, m = m, print = FALSE)
imp.data.list <- mice::complete(imp.out, "all")
#Match within each imputed dataset
cem.out.imp <- cem::cem("treat", datalist = imp.data.list,
drop = "re78")
bal.tab(cem.out.imp, data = imp.out)bal.tab() with other packagesIt is possible to use bal.tab() with objects that don’t
come from these packages using the default method. If an
object that doesn’t correspond to the output from one of the
specifically supported packages is passed as the first argument to
bal.tab(), bal.tab() will do its best to
process that object as if it did come from a supported package. It will
search through the components of the object for items with names like
"treat", "covs", "data",
"weights", etc., that have the correct object types. Any
additional arguments can be specified by the user.
The goal of the default method is to allow package
authors to rely on cobalt as a substitute for any balancing
function they might otherwise write. By ensuring compatibility with the
default method, package authors can have their users simply
supply the output of a compatible function into cobalt
functions without having to write a specific method in
cobalt. A package author would need to make sure the output
of their package contained enough information with correctly named
components; if so, cobalt functions can be used as
conveniently with the output as it is with specifically supported
packages.
Below, we demonstrate this capability with the output of
optweight, which performs a version of propensity score
weighting using optimization, similar to sbw. No
bal.tab method has been written with optweight
output in mind; rather, optweight was written to have
output compatible with the default method of
bal.tab.
#Optimization-based weighting
data("lalonde", package = "cobalt")
#Estimate the weights using optimization
ow.out <- optweight::optweight(treat ~ age + educ + married + race + re74 + re75,
data = lalonde, estimand = "ATE", tols = .01)
#Note the contents of the output object:
names(ow.out)## [1] "weights" "treat" "covs" "s.weights" "b.weights" "estimand"
## [7] "focal" "call" "tols" "duals" "info"## Balance Measures
## Type Diff.Adj
## age Contin. -0.0000
## educ Contin. 0.0100
## married Binary -0.0223
## race_black Binary 0.0261
## race_hispan Binary -0.0000
## race_white Binary -0.0252
## re74 Contin. -0.0100
## re75 Contin. 0.0085
##
## Effective sample sizes
## Control Treated
## Unadjusted 429. 185.
## Adjusted 349.42 52.04The output is treated as output from a specifically supported
package. See ?bal.tab.default for more details and another
example.