MatchIt News and
UpdatesFixed a bug where including an irrelevant argument to
matchit() would yield the spurious error “invalid ‘pos’
argument”. Thanks to @raffaem. (#219)
Improved performance of method = "nearest" when
calipers are included on covariates.
Added some new tests.
Updates for CRAN compatibility.
Typo fixes in documentation and vignettes, thanks to @iagogv3.
For nearest neighbor matching, optimal full matching, and genetic matching, calipers can now be negative, which forces paired units to be further away from each other on the given variables.
When using method = "optimal" to do 1:1 matching on
a propensity score, a new preprocessing algorithm is used to speed up
the matching process. This algorithm ensures the resulting match is as
good as a match that would have been found without the preprocessing
step while shrinking the size of the matching problem. Typically, the
same matched set will be selected, but units may be paired differently
(this is because the general optimization problem often has multiple
solutions with the same value of the objective function; this algorithm
adds an additional constraint to select among fewer such solutions).
This algorithm is described in Sävje (2020) and is
implemented in C++ to be fast.
Fixed a bug when matching with a nonzero ratio where
subclass membership was incorrectly calculated. Thanks to Simon Loewe
(@simon-lowe) for
originally pointing it out. (#207, #208)
match.data() has been renamed to
match_data(), but match.data() will remain as
an alias for backward compatibility.
Fixed a bug with printing.
Documentation fixes.
Most improvements are related to performance. Some of these
dramatically improve speeds for large datasets. Most come from
improvements to Rcpp code.
When using method = "nearest", m.order
can now be set to "farthest" to prioritize hard-to-match
treated units. Note this does not implement “far
matching” but simply changes the order in which the closest matches are
selected.
Speed improvements to method = "nearest", especially
when matching on a propensity score.
Speed improvements to summary() when
pair.dist = TRUE and a match.matrix component
is not included in the output (e.g., for method = "full" or
method = "quick").
Speed improvements to method = "subclass" with
min.n greater than 0.
A new normalize argument has been added to
matchit(). When set to TRUE (the default,
which used to be the only option), the nonzero weights in each treatment
group are rescaled to have an average of 1. When FALSE, the
weights generated directly by the matching are returned
instead.
When using method = "nearest" with
m.order = "closest", the full distance matrix is no longer
computed, which increases support for larger samples. This uses an
adaptation of an algorithm described by Rassen et
al. (2012).
When using method = "nearest" with
verbose = TRUE, the progress bar now displays an estimate
of how much time remains.
When using method = "nearest" with
m.order = "closest" and ratio greater than 1,
all eligible units will receive their first match before any receive
their second, etc. Previously, the closest pairs would be matched
regardless of whether other units had been matched. This ensures
consistency with other m.order arguments.
Speed and memory improvements to method = "cem" with
many covariates and a large sample size. Previous versions used a
Cartesian expansion of all levels of factor variables, which could
easily explode.
When using method = "cem" with
k2k = TRUE, m.order can be set to select the
matching order. Allowable options include "data" (the
default), "closest", "farthest", and
"random". "closest" is recommended, but
"data" is the default for now to remain consistent with
previous versions.
Documentation updates.
Fixed a bug when using method = "optimal" or
method = "full" with discard specified and
data given as a tibble (tbl_df object).
(#185)
Fixed a bug when using method = "cardinality" with a
single covariate. (#194)
When using method = "cardinality", a new solver,
HiGHS, can be requested by setting solver = "highs", which
relies on the highs package. This is much faster and more
reliable than GLPK and is free and easy to install as a regular R
package with no additional requirements.
Fixed a bug when using method = "optimal" with
discard and exact specified. Thanks to @NikNakk for the issue and
fix. (#171)
With method = "nearest", m.order can
now be set to "closest" to request that the closest
potential pairs are matched first. This can be used whether a propensity
score is used or not.
Fixed bugs when distance = NULL and no covariates
are specified in matchit().
Changed “empirical cumulative density function” to “empirical cumulative distribution function” in documentation. (#166)
Fixed a bug where calipers would not work properly on some systems. Thanks to Bill Dunlap for the solution. (#163)
Fixed a bug when . was present in formulas. Thanks
to @dmolitor.
(#167)
Fixed a bug when nearest neighbor matching for the ATC with
distance supplied as a numeric distance matrix.
Error messages have been improved using chk and
rlang, which are now dependencies.
Fixed a bug when using method = "nearest" with
replace = TRUE and ratio greater than 1.
Thanks to Julia Kretschmann. (#159)
Fixed a bug when using method = "nearest" with
exact and ratio greater than 1. Thanks to
Sarah Conner.
Fixed a bug that would occur due to numerical imprecision in
plot.matchit(). Thanks to @hkmztrk. (#158)
Fixed bugs when using method = "cem" where a
covariate was to be omitted from coarsening. Thanks to @jfhelmer. (#160)
Fixed some typos in the vignettes. Thanks to @fBedecarrats. (#156)
Updated vignettes to use marginaleffects v0.11.0
syntax.
Fixed a bug when using method = "quick" with
exact specified. Thanks to @m-marquis. (#149)
Improved performance and fixed some bugs when using
exact in cases where some strata contain units from only
one treatment group. Thanks to @m-marquis and others for pointing these
out. (#151)
Nearest neighbor matching now uses a much faster algorithm (up to
6x times faster) when distance is a propensity score and
mahvars is not specified. Differences in sort order might
cause results to differ from previous versions if there are units with
identical propensity scores.
Template matching has been renamed profile matching in all documentation.
After cardinality or profile matching using
method = "cardinality" with ratio set to a
whole number, it is possible to perform optimal Mahalanobis distance
matching in the matched sample by supplying the desired matching
variables to mahvars. Previously, the user had to run a
separate pairing step.
Fixed some typos in the vignettes.
Fixed a bug where character variables would be flagged as non-finite. Thanks to @isfraser. (#138)
Added alt text to images in README and vignettes. (#134)
Generalized full matching, as described by Sävje, Higgins, and Sekhon
(2021), can now be implemented by setting
method = "quick" in matchit(). It is a
dramatically faster alternative to optimal full matching that can
support much larger datasets and otherwise has similar balancing
performance. See ?method_quick and
vignette("matching-methods") for more information. This
functionality relies on the quickmatch package.
The package structure has been updated, include with the use of Roxygen for documentation. This should not affect use, but the source code will look different from that of previous versions.
When method = "subclass" and min.n = 0
(which is not the default), any units not placed into a subclass are now
considered “unmatched” and given weights of 0. Previously they were left
in.
When method = "genetic", the default
distance.tolerance is now 0. In previous versions, this
argument was ignored; now it is not.
For plot.matchit(), the which.xs
argument can be specified as a one-sided formula. A new
data argument is allowed if the variables in that formula
are not among the original covariates.
When a factor variable is supplied to plot.matchit()
with type = "density", the plot now displays all factor
levels in the same plot instead of in separate plots for each level,
similar to cobalt::bal.plot().
The “Estimating Effects” vignette
(vignette("estimating-effects")) has been rewritten to be
much shorter (and hopefully clearer) and to use the
marginaleffects package, which is now a Suggested package.
The new vignette focuses on using g-computation to estimate treatment
effects using a single workflow with slight modifications for different
situations.
The error message when covariates have missing or non-finite values is now clearer, identifying which variables are afflicted. This fixes a bug mentioned in #115.
Fixed a bug when using matchit() with
method = "cem", k2k = TRUE, and
k2k.method = NULL. Thanks to Florian B. Mayr.
Fixed a bug when using method = "optimal" and
method = "full" with exact and
antiexact specified, wherein a warning would occur about
the drop argument in subsetting.
Fixed a bug where antiexact would not work correctly
with method = "nearest". Thanks to @gli-1. (#119)
Fixed typos in the documentation and vignettes.
Calculating pair distances in summary() with
pair.dist = TRUE is now faster.
Improved printing of balance results when no covariates are supplied.
Updates to the Estimating Effects vignette that dramatically increase the speed of the cluster bootstrap for average marginal effects after matching. Thanks to Yohei Hashimoto for pointing out the inefficiency.
Updates to the Assessing Balance vignette to fix errors
All vignettes and help files are better protected against Suggested packages not available on CRAN.
optmatch has returned to CRAN, now with an
open-source license! A new solver argument can be passed to
matchit() with method = "full" and
method = "optimal" to control the solver used to perform
the optimization used in the matching. Note that using the default (open
source) solver LEMON may yield results different from those obtained
prior to optmatch 0.10.0. For reproducibility questions,
please contact the optmatch maintainers.
New functions have been added to compute the Euclidean distance
(euclidean_dist()), scaled Euclidean distance
(scaled_euclidean_dist()), Mahalanobis distance
(mahalanobis_dist()), and robust Mahalanobis distance
(robust_mahalanobis_dist()). They produce distance matrices
that can be supplied to the distance argument of
matchit(), but see below.
New distance options are available for matchit()
based on the distance functions above:
"robust_mahalanobis", "euclidean", and
"scaled_euclidean", which complement
"mahalanobis". Similar to "mahalanobis", these
do not involve estimating a propensity score but rather operate on the
covariates directly. These can be used for nearest neighbor matching,
optimal matching, full matching, and coarsened exact matching with
k2k = TRUE.
The Mahalanobis distance is now computed using the pooled
within-group covariance matrix (computed by treatment group-mean
centering each covariate before computing the covariance in the full
sample), in line with how it is computed in optmatch and
recommended by Rubin (1980) among others. This will cause results to
differ between this version and prior versions of MatchIt
that used the Mahalanobis distance computed ignoring group
membership.
Added the unit.id argument to matchit()
with method = "nearest", which defines unit IDs so that if
a control observation with a given unit ID has been matched to a treated
unit, no other control units with the same ID can be used as future
matches, ensuring each unit ID is used no more than once. This is useful
when, e.g., multiple rows correspond to the same control firm but you
only want each control firm to be matched once, in which case firm ID
would be supplied to unit.id. See here for an
example use case.
In summary.matchit(), improvement is
now set to FALSE by default to hide the percentage
improvement in balance. Set to TRUE to recover prior
behavior.
Added clearer errors when required packages are missing for
certain distance methods.
Fixed a bug when using matchit() with
method = "nearest", ratio greater than 1, and
reuse.max specified. The bug allowed a previously matched
control unit to be matched to the same treatment unit, thereby
essentially ignoring the ratio argument. It now works as
intended.
Fixed a bug in matchit() with
method = "nearest" when distance was supplied
as a matrix and Inf values were present.
Fixed a bug when using exact matching that caused an infinite loop when variable levels contained commas. Thanks to @bking124. (#111)
Fixed a bug introduced by optmatch version
0.10.3.
Documentation updates.
Updated the logo, thanks to Ben Stillerman.
optmatch has been removed from CRAN. Instructions on
installing it are in ?method_optimal and
?method_full.
When s.weights are supplied with
distance = "randomforest", the weights are supplied to
randomForest::randomForest().
Improved conditional use of packages, especially
optmatch. This may mean that certain examples fail to run
in the vignettes.
rbind.matchdata() would produce
datasets twice their expected length. Thanks to @sconti555. (#98)Fixed a bug where the q.cut component of the
matchit object when method = "subclass" was
not included. Now it is. Thanks to @aldencabajar. (#92)
The nn and qn components of the
matchit object have been removed. They are now computed by
summary.matchit() and included in the
summary.matchit object.
Removed the code to disable compiler checks to satisfy CRAN requirements.
Added the reuse.max argument to
matchit() with method = "nearest". This
controls the maximum number of times each control unit can be used as a
match. Setting reuse.max = 1 is equivalent to matching
without replacement (i.e., like setting replace = FALSE),
and setting reuse.max = Inf is equivalent to matching with
replacement with no restriction on the reuse of controls (i.e., like
setting replace = TRUE). Values in between restrict how
many times each control unit can be used as a match. Higher values will
tend to improve balance but decrease precision.
Mahalanobis distance matching with
method = "nearest" is now a bit faster.
Fixed a bug where method = "full" would fail when
some exact matching strata contained exactly one treated unit and
exactly one control unit. (#88)
Fixed a bug introduced in 4.3.0 where the inclusion of character
variables would cause the error
"Non-finite values are not allowed in the covariates."
Thanks to Moaath Mustafa.
Documentation updates.
Cardinality and template matching can now be used by setting
method = "cardinality" in matchit(). These
methods use mixed integer programming to directly select a matched
subsample without pairing or stratifying units that satisfied
user-supplied balance constraints. Their results can be dramatically
improved when using the Gurobi optimizer. See
?method_cardinality and
vignette("matching-methods") for more information.
Added "lasso", "ridge", and
"elasticnet" as options for distance. These
estimate propensity scores using lasso, ridge, or elastic net
regression, respectively, as implemented in the glmnet
package.
Added "gbm" as an option for distance.
This estimates propensity scores using generalized boosted models as
implemented in the gbm package. This implementation differs
from that in twang by using cross-validation or out-of-bag
error to choose the tuning parameter as opposed to balance.
A new argument, include.obj, has been added to
matchit(). When TRUE, the intermediate
matching object created internally will be included in the output in the
obj component. See the individual methods pages for
information on what is included in each output. This is ignored for some
methods.
Density plots can now be requested using
plot.matchit() by setting type = "density".
These display the density of each covariate in the treatment groups
before and after matching and are similar to the plots created by
cobalt::bal.plot(). Density plots can be easier to
interpret than eCDF plots. vignette("assessing-balance")
has been updated with this addition.
A clearer error is now produced when the treatment variable is
omitted from the formula argument to
matchit().
Improvements in how match.data() finds the original
dataset. It’s still always safer to supply an argument to
data, but now match.data() will look in the
environment of the matchit formula, then the calling
environment of match.data(), then the model
component of the matchit object. A clearer error message is
now printed when a valid dataset cannot be found in these
places.
Fixed a bug that would occur when using
summary.matchit() with just one covariate.
When verbose = TRUE and a propensity score is
estimated (i.e., using the distance argument), a message
saying so will be displayed.
Fixed a bug in print.matchit() where it would
indicate that the propensity score was used in a caliper if any caliper
was specified, even if not on the propensity score. Now, it will only
indicate that the propensity score was used in a caliper if it actually
was.
Fixed a bug in plot.matchit() that would occur when
a level of a factor had no values.
Speed improvements for method = "full" with
exact specified. These changes can make current results
differ slightly from past results when the tol value is
high. It is recommended to always use a low value of
tol.
Typo fixes in documentation and vignettes.
Fixed a bug where supplying a “GAM” string to the
distance argument (i.e., using the syntax prior to version
4.0.0) would ignore the link supplied.
When an incompatible argument is supplied to
matchit() (e.g., reestimate with
distance = "mahalanobis"), an error or warning will only be
produced when that argument has been set to a value other than its
default (e.g., so setting reestimate = FALSE will no longer
throw an error). This fixes an issue brought up by Vu Ng when using
MatchThem.
A clearer error is produced when non-finite values are present in the covariates.
distance can now be supplied as a distance matrix
containing pairwise distances with nearest neighbor, optimal, and full
matching. This means users can create a distance matrix outside
MatchIt (e.g., using optmatch::match_on() or
dist()) and matchit() will use those distances
in the matching. See ?distance for details.
Added rbind.matchdata() method for
matchdata and getmatches objects (the output
of match.data() and get_matches(),
respectively) to avoid subclass conflicts when combining matched samples
after matching within subgroups.
Added a section in vignette("estimating-effects") on
moderation analysis with matching, making use of the new
rbind() method.
Added antiexact argument to perform anti-exact
matching, i.e., matching that ensures treated and control units have
different values of certain variables. See here and here for
examples where this feature was requested and might be useful.
Anti-exact matching works with nearest neighbor, optimal, full, and
genetic matching. The argument to antiexact should be
similar to an argument to exact: either a string or a
one-sided formula containing the names of the anti-exact
matching variables.
Slight speed improvements for nearest neighbor matching,
especially with exact specified.
With method = "nearest",
verbose = TRUE, and exact specified, separate
messages and progress bars will be shown for each subgroup of the
exact variable(s).
A spurious warning that would appear when using a large
ratio with replace = TRUE and
method = "nearest" no longer appears.
Fixed a bug when trying to supply distance as a
labeled numeric vector (e.g., resulting from
haven).
Fixed some typos in the documentation and vignettes.
Coarsened exact matching (i.e., matchit() with
method = "cem") has been completely rewritten and no longer
involves the cem package, eliminating some spurious warning
messages and fixing some bugs. All the same arguments can still be used,
so old code will run, though some results will differ slightly.
Additional options are available for matching and performance has
improved. See ?method_cem for details on the differences
between the implementation in the current version of
MatchIt and that in cem and older versions of
MatchIt. In general, these changes make coarsened exact
matching function as one would expect it to, circumventing some
peculiarities and bugs in the cem package.
Variable ratio matching is now compatible with
method = "optimal" in the same way it is with
method = "nearest", i.e., by using the
min.controls and max.controls
arguments.
With method = "full" and
method = "optimal", the maximum problem size has been set
to unlimited, so that larger datasets can be used with these methods
without error. They may take a long time to run, though.
Processing improvements with method = "optimal" due
to rewriting some functions in Rcpp.
Using method = "optimal" runs more smoothly when
combining it with exact matching through the exact
argument.
When using ratio different from 1 with
method = "nearest" and method = "optimal" and
with exact matching, errors and warnings about the number of units that
will be matched are clearer. Certain ratios that would
produce errors now only produce warnings.
Fixed a bug when no argument was supplied to data in
matchit().
Improvements to vignettes and documentation.
Restored cem functionality after it had been taken
down and re-uploaded.
Added pkgdown website.
Computing matching weights after matching with replacement is
faster due to programming in Rcpp.
Fixed issues with Rcpp code that required C++11.
C++11 has been added to SystemRequirements in DESCRIPTION, and
MatchIt now requires R version 3.1.0 or later.
match.data(), which is used to create matched
datasets, has a few new arguments. The data argument can be
supplied with a dataset that will have the matching weights and
subclasses added. If not supplied, match.data() will try to
figure out the appropriate dataset like it did in the past. The
drop.unmatched argument controls whether unmatched units
are dropped from the output. The default is TRUE,
consistent with past behavior. Warnings are now more
informative.
get_matches(), which seems to have been rarely used
since it performed a similar function to match.data(), has
been revamped. It creates a dataset with one row per unit per matched
pair. If a unit is part of two separate pairs (e.g., as a result of
matching with replacement), it will get two rows in the output dataset.
The goal here was to be able to implement standard error estimators that
rely both on repeated use of the same unit and subclass/pair membership,
e.g., Austin & Cafri (2020). Otherwise, it functions similarly to
match.data(). NOTE: the changes to
get_matches() are breaking changes! Legacy code will not
work with the new syntax!
print.matchit() has completely changed and now
prints information about the matching type and specifications.
summary.matchit() contains all the information that was in
the old print method.
A new function, add_s.weights(), adds sampling
weights to matchit objects for use in balance checking and
effect estimation. Sampling weights can also be directly supplied to
matchit() through the new s.weights argument.
A new vignette describing how to using MatchIt with
sampling weights is available at
vignette("sampling-weights").
The included dataset, lalonde, now uses a
race variable instead of separate black and
hispan variables. This makes it easier to see how character
variables are treated by MatchIt functions.
Added extensive documentation for every function, matching
method, and distance specification. Documentation no longer links to
gking.harvard.edu/matchit as it now stands alone.
matchit()An argument to data is no longer required if the
variables in formula are present in the
environment.
When missing values are present in the dataset but not in the treatment or matching variables, the error that used to appear no longer does.
The exact argument can be supplied either as a
character vector of names of variables in data or as a
one-sided formula. A full cross of all included variables will be used
to create bins within which matching will take place.
The mahvars argument can also be supplied either as
a character vector of names of variables in data or as a
one-sided formula. Mahalanobis distance matching will occur on the
variables in the formula, processed by model.matrix(). Use
this when performing Mahalanobis distance matching on some variables
within a caliper defined by the propensity scores estimated from the
variables in the main formula using the argument to
distance. For regular Mahalanobis distance matching
(without a propensity score caliper), supply the variables in the main
formula and set
distance = "mahalanobis".
The caliper argument can now be specified as a
numeric vector with a caliper for each variable named in it. This means
you can separately impose calipers on individual variables as well as or
instead of the propensity score. For example, to require that units
within pairs must be no more than .2 standard deviations of
X1 away from each other, one could specify
caliper = c(X1 = .2). A new option std.caliper
allows the choice of whether the caliper is in standard deviation units
or not, and one value per entry in caliper can be supplied.
An unnamed entry to caliper applies the caliper to the
propensity score and the default of std.caliper is
FALSE, so this doesn’t change the behavior of old code.
These options only apply to the methods that accept calipers, namely
"nearest", "genetic", and
"full".
A new estimand argument can be supplied to specify
the target estimand of the analysis. For all methods, the ATT and ATC
are available with the ATT as the default, consistent with prior
behavior. For some methods, the ATE is additionally available. Note that
setting the estimand doesn’t actually mean that estimand is being
targeted; if calipers, common support, or other restrictions are
applied, the target population will shift from that requested.
estimand just triggers the choice of which level of the
treatment is focal and what formula should be used to compute weights
from subclasses.
In methods that accept it, m.order can be set to
“data”, which matches in the order the data appear. With
distance = "mahalanobis", m.order can be
“random” or “data”, with “data”
as the default. Otherwise, m.order can be
"largest", "smallest", "random",
or "data", with "largest" as the default
(consistent with prior behavior).
The output to matchit() has changed slightly; the
component X is now a data frame, the result of a call to
model.frame() with the formula provided. If
exact or mahvars are specified, their
variables are included as well, if not already present. It is included
for all methods and is the same for all methods. In the past, it was the
result of a call to model.matrix() and was only included
for some methods.
When key arguments are supplied to methods that don’t accept them, a warning will be thrown.
method can be set to NULL to not
perform matching but create a matchit object, possibly with
a propensity score estimated using distance or with a
common support restriction using discard, for the purpose
of supplying to summary.matchit() to assess balance prior
to matching.
method = "nearest"Matching is much faster due to re-programming with
Rcpp.
With method = "nearest", a subclass
component containing pair membership is now included in the output when
replace = FALSE (the default), as it has been with optimal
and full matching.
When using method = "nearest" with
distance = "mahalanobis", factor variables can now be
included in the main formula. The design matrix no longer
has to be full rank because a generalized inverse is used to compute the
Mahalanobis distance.
Unless m.order = "random", results will be identical
across runs. Previously, several random choices would occur to break
ties. Ties are broken based on the order of the data; shuffling the
order of the data may therefore yield different matches.
When using method = "nearest" with a caliper
specified, the nearest control unit will be matched to the treated unit
if one is available. Previously, a random control unit within the
caliper would be selected. This eliminates the need for the
calclosest argument, which has been removed.
Variable ratio extremal matching as described by Ming &
Rosenbaum (2000) can be implemented using the new
min.controls and max.controls
arguments.
Added ability to display a progress bar during matching, which
can be activated by setting verbose = TRUE.
method = "optimal"
and method = "full"Fixed bug in method = "optimal", which produced
results that did not match optmatch. Now they do.
Added support for optimal and full Mahalanobis distance matching
by setting method = "mahalanobis" with
method = "optimal" and method = "full".
Previously, both methods would perform a random match if
method was set to "mahalanobis". Now they use
the native support in optmatch::pairmatch() and
optmatch::fullmatch() for Mahalanobis distance
matching.
Added support for exact matching with
method = "optimal" and method = "full". As
with method = "nearest", the names of the variables for
which exact matches are required should be supplied to the
exact argument. This relies on
optmatch::exactMatch().
The warning that used to occur about the order of the match not guaranteed to be the same as the original data no longer occurs.
For method = "full", the estimand
argument can be set to "ATT", "ATC", or
"ATE" to compute matching weights that correspond to the
given estimand. See ?matchit for details on how weights are
computed for each estimand.
method = "genetic"Fixed a bug with method = "genetic" that caused an
error with some ratio greater than 1.
The default of replace in
method = "genetic" is now FALSE, as it is with
method = "nearest".
When verbose = FALSE, the default, no output is
printed with method = "genetic". With
verbose = TRUE, the printed output of
Matching::GenMatch() with print.level = 2 is
displayed.
The exact argument now correctly functions with
method = "genetic". Previously, it would have to be
specified in accordance with its use in
Matching::GenMatch().
Different ways to match on variables are now allowed with
method = "genetic", similar to how they are with
method = "nearest". If
distance = "mahalanobis", no propensity score will be
computed, and genetic matching will be performed just on the variables
supplied to formula. If mahvars is specified,
genetic matching will be performed on the variables supplied to
mahvars, but balance will be optimized on all covariates
supplied to formula. Otherwise, genetic matching will be
performed on the variables supplied to formula and the
propensity score. Previously, mahvars was ignored. Balance
is now always optimized on the variables included in
formula and never on the propensity score, whereas in the
past the propensity score was always included in the balance
optimization.
The caliper argument now works as it does with
method = "nearest" and other methods rather than needing to
be supplied in a way that Matching::Match() would
accept.
A subclass component is now included in the output
when replace = FALSE (the default), as it has been with
optimal and full matching.
method = "cem" and
method = "exact"With method = "cem", the k2k argument
is now recognized. Previously it was ignored unless an argument to
k2k.method was supplied.
The estimand argument can be set to
"ATT", "ATC", or "ATE" to compute
matching weights that correspond to the given estimand. Previously only
ATT weights were computed. See ?matchit for details on how
weights are computed for each estimand.
method = "subclass"Performance improvements.
A new argument, min.n, can be supplied, which
controls the minimum size a treatment group can be in each subclass.
When any estimated subclass doesn’t have enough members from a treatment
group, units from other subclasses are pulled to fill it so that every
subclass will have at least min.n units from each treatment
group. This uses the same mechanism as is used in WeightIt.
The default min.n is 1 to ensure there are at least one
treated and control unit in each subclass.
Rather than producing warnings and just using the default number
of subclasses (6), when an inappropriate argument is supplied to
subclass, an error will occur.
The new subclass argument to summary()
can be used to control whether subclass balance statistics are computed;
it can be TRUE (display balance for all subclasses),
FALSE (display balance for no subclasses), or a vector of
subclass indices on which to assess balance. The default is
FALSE.
With summary(), balance aggregating across
subclasses is now computed using subclass weights instead of by
combining the subclass-specific balance statistics.
The sub.by argument has been replaced with
estimand, which can be set to "ATT",
"ATC", or "ATE" to replace the
sub.by inputs of "treat",
"control", and "all", respectively.
Previously, weights for sub.by that wasn’t
"treat" were incorrect; they are now correctly computed for
all inputs to estimand.
distanceThe allowable options to distance have changed
slightly. The input should be either "mahalanobis" for
Mahalanobis distance matching (without a propensity score caliper), a
numeric vector of distance values (i.e., values whose absolute pairwise
differences form the distances), or one of the allowable options. The
new allowable values include "glm" for propensity scores
estimated with glm(), "gam" for propensity
scores estimated with mgcv::gam(), "rpart" for
propensity scores estimated with rpart::rpart(),
"nnet" for propensity scores estimated with
nnet::nnet(), "cbps" for propensity scores
estimated with CBPS::CBPS(), or bart for
propensity scores estimated with dbarts::bart2(). To
specify a link (e.g., for probit regression), specify an argument to the
new link parameter. For linear versions of the propensity
score, specify link as "linear.{link}". For
example, for linear probit regression propensity scores, one should
specify distance = "glm", link = "linear.probit". The
default distance is "glm" and the default link
is "logit", so these can be omitted if either is desired.
Not all methods accept a link, and for those that don’t, it
will be ignored. If an old-style distance is supplied, it
will be converted to an appropriate specification with a warning (except
for distance = "logit", which will be converted without a
warning).
Added "cbps" as option for distance.
This estimates propensity scores using the covariate balancing
propensity score (CBPS) algorithm as implemented in the
CBPS package. Set link = "linear" to use a
linear version of the CBPS.
Added "bart" as an option for distance.
This estimates propensity scores using Bayesian Additive Regression
Trees (BART) as implemented in the dbarts package.
Added "randomforest" as an option for
distance. This estimates propensity scores using random
forests as implemented in the randomForest
package.
Bugs in distance = "rpart" have been fixed.
summary.matchit()When interactions = TRUE, interactions are no longer
computed with the distance measure or between dummy variables of the
same factor. Variable names are cleaned up and easier to read.
The argument to addlvariables can be specified as a
data frame or matrix of covariates, a formula with the additional
covariates (and transformations) on the right side, or a character
vector containing the names of the additional covariates. For the latter
two, if the variables named do not exist in the X component
of the matchit output object or in the environment, an
argument to data can be supplied to summary()
that contains these variables.
The output for summary() is now the same for all
methods (except subclassification). Previously there were different
methods for a few different types of matching.
The eCDF median (and QQ median) statistics have been replaced with the variance ratio, which is better studied and part of several sets of published recommendations. The eCDF and QQ median statistics provide little information above and beyond the corresponding mean statistics. The variance ratio uses the variances weighted by the matching weights.
The eCDF and QQ statistics have been adjusted. Both now use the weights that were computed as part of the matching. The eCDF and QQ statistics for binary variables are set to the difference in group proportions. The standard deviation of the control group has been removed from the output.
The default for standardize is now
TRUE, so that standardized mean differences and eCDF
statistics will be displayed by default.
A new column for the average absolute pair difference for each
covariate is included in the output. The values indicate how far treated
and control units within pairs are from each other. An additional
argument to summary.matchit(), pair.dist,
controls whether this value is computed. It can take a long time for
some matching methods and could be omitted to speed up
computation.
Balance prior to matching can now be suppressed by setting
un = FALSE.
Percent balance improvement can now be suppressed by setting
improvement = FALSE. When un = FALSE,
improvement is automatically set to
FALSE.
plot.matchit()Plots now use weighted summaries when weights are present,
removing the need for the num.draws argument.
Added a new plot type, "ecdf", which creates
empirical CDF plots before and after matching.
The appearance of some plots has improved (e.g., text is appropriately centered, axes are more clearly labeled). For eQQ plots with binary variables or variables that take on only a few values, the plots look more like clusters than snakes.
The argument to type can be abbreviated (e.g.,
"j" for jitter).
Fixed a bug that caused all plots generated after using
plot(., type = "hist") to be small.
When specifying an argument to which.xs to control
for which variables balance is displayed graphically, the input should
be the name of the original variable rather than the version that
appears in the summary() output. In particular, if a factor
variable was supplied to matchit(), it should be referred
to by its name rather than the names of its split dummies. This makes it
easier to view balance on factor variables without having to know or
type the names of all their levels.
eQQ plots can now be used with all matching methods. Previously,
attempting plot() after method = "exact" would
fail.
plot.summary.matchit()graphics::dotchart(). A few options are
available for ordering the variables, presenting absolute or raw
standardized mean differences, and placing threshold lines on the plots.
For a more sophisticated interface, see
cobalt::love.plot(), which natively supports
matchit objects and uses ggplot2 as its
engine.
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