Generates weights to form equivalent groups in observational studies with point or longitudinal treatments by easing and extending the functionality of the R packages 'twang' for generalized boosted modeling (McCaffrey, Ridgeway & Morral, 2004)
WeightIt
is a one-stop package to generate balancing weights for point
and longitudinal treatments in observational studies. Contained within
WeightIt
are methods that call on other R packages to estimate
weights. The value of WeightIt
is in its unified and familiar syntax
used to generate the weights, as each of these other packages have their
own, often challenging to navigate, syntax. WeightIt
extends the
capabilities of these packages to generate weights used to estimate the
ATE, ATT, and ATC for binary or multinomial treatments, and treatment
effects for continuous treatments when available. In these ways,
WeightIt
does for weighting what MatchIt
has done for matching, and
MatchIt
users will find the syntax familiar.
To install and load WeightIt
, use the code below:
install.packages("WeightIt") #CRAN versiondevtools::install_github("ngreifer/WeightIt") #Development versionlibrary("WeightIt")
The workhorse function of WeightIt
is weightit()
, which generates
weights from a given formula and data input according to methods and
other parameters sepcified by the user. Below is an example of the use
of weightit()
to generate weights for estimating the ATE:
data("lalonde", package = "cobalt")W <- weightit(treat ~ age + educ + nodegree + married + race + re74 + re75,data = lalonde, method = "ps", estimand = "ATE")print(W)
A weightit object
- method: "ps" (propensity score weighting)
- number of obs.: 614
- sampling weights: none
- treatment: 2-category
- estimand: ATE
- covariates: age, educ, nodegree, married, race, re74, re75
Evaluating weights has two components: evaluating the covariate balance
produces by the weights, and evaluating whether the weights will allow
for sufficient precision in the eventual effect estimate. For the first
goal, functions in the cobalt
package, which are fully compatible with
WeightIt
, can be used, as demonstrated below:
library("cobalt")bal.tab(W, un = TRUE)
Call
weightit(formula = treat ~ age + educ + nodegree + married +
race + re74 + re75, data = lalonde, method = "ps", estimand = "ATE")
Balance Measures
Type Diff.Un Diff.Adj
prop.score Distance 1.7569 0.1360
age Contin. -0.2419 -0.1676
educ Contin. 0.0448 0.1296
nodegree Binary 0.1114 -0.0547
married Binary -0.3236 -0.0944
race_black Binary 0.6404 0.0499
race_hispan Binary -0.0827 0.0047
race_white Binary -0.5577 -0.0546
re74 Contin. -0.5958 -0.2740
re75 Contin. -0.2870 -0.1579
Effective sample sizes
Control Treated
Unadjusted 429.000 185.000
Adjusted 329.008 58.327
For the second goal, qualities of the distributions of weights can be
assessed using summary()
, as demonstrated below.
summary(W)
Summary of weights:
- Weight ranges:
Min Max
treated 1.1721 |---------------------------| 40.0773
control 1.0092 |-| 4.7432
- Units with 5 greatest weights by group:
137 124 116 68 10
treated 13.5451 15.9884 23.2967 23.3891 40.0773
597 573 411 381 303
control 4.0301 4.0592 4.2397 4.5231 4.7432
Ratio Coef of Var
treated 34.1921 1.4777
control 4.7002 0.5519
overall 39.7134 1.3709
- Effective Sample Sizes:
Control Treated
Unweighted 429.000 185.000
Weighted 329.008 58.327
Desirable qualities include ratios close to 1, coefficients of variation close to 0, and large effective sample sizes.
The table below contains the available methods in WeightIt
for
estimating weights for binary, multinomial, and continuous treatments
using various methods and functions from various
packages.
Treatment type | Method (method = ) |
Function | Package |
---|---|---|---|
Binary | Binary regression PS ("ps" ) |
glm() |
base |
- | Generalized boosted modeling PS ("gbm" ) |
ps() |
twang |
- | Covariate Balancing PS ("cbps" ) |
CBPS() |
CBPS |
- | Non-Parametric Covariate Balancing PS ("npcbps" ) |
npCBPS() |
CBPS |
- | Entropy Balancing ("ebal" ) |
ebalance() |
ebal |
- | Empirical Balancing Calibration Weights ("ebcw" ) |
ATE() |
ATE |
- | Optimization-Based Weights ("optweight" ) |
optweight() |
optweight |
- | SuperLearner PS ("super" ) |
SuperLearner() |
SuperLearner |
Multinomial | Multiple binary regression PS ("ps" ) |
glm() |
base |
- | Multinomial regression PS ("ps" ) |
mlogit() |
mlogit |
- | Bayesian multinomial regression PS ("ps", link = "bayes.probit" ) |
MNP() |
MNP |
- | Generalized boosted modeling PS ("gbm" ) |
ps() |
twang |
- | Covariate Balancing PS ("cbps" ) |
CBPS() |
CBPS |
- | Non-Parametric Covariate Balancing PS ("npcbps" ) |
npCBPS() |
CBPS |
- | Entropy Balancing ("ebal" ) |
ebalance() |
ebal |
- | Stable Balancing Weights ("sbw" ) |
sbw() |
sbw |
- | Empirical Balancing Calibration Weights ("ebcw" ) |
ATE() |
ATE |
- | Optimization-Based Weights ("optweight" ) |
optweight() |
optweight |
- | SuperLearner PS ("super" ) |
SuperLearner() |
SuperLearner |
Continuous | Generalized linear model PS ("ps" ) |
glm() |
base |
- | Generalized boosted modeling PS ("gbm" ) |
ps.cont() |
WeightIt |
- | Covariate Balancing PS ("cbps" ) |
CBPS() |
CBPS |
- | Non-Parametric Covariate Balancing PS ("npcbps" ) |
npCBPS() |
CBPS |
- | Optimization-Based Weights ("optweight" ) |
optweight() |
optweight |
- | SuperLearner PS ("super" ) |
SuperLearner() |
SuperLearner |
If you would like to see your package or method integrated into
WeightIt
, or for any other questions or comments about WeightIt
,
please contact Noah Greifer at [email protected].
Version 0.5.1
Fixed a bug when using the ps
argument in weightit()
.
Fixed a bug when setting include.obj = TRUE
in weightitMSM()
.
Added warnings for using certain methods with longitudinal treatments as they are not validated and may lead to incorrect inferences.
Version 0.5.0
Added super
method to estimate propensity scores using the SuperLearner
package.
Added optweight
method to estimate weights using optimization (but you should probably just use the optweight
package).
weightit()
now uses the correct formula to estimate weights for the ATO with multinomial treatments as described by Li & Li (2018).
Added include.obj
option in weightit()
and weightitMSM()
to include the fitted object in the output object for inspection. For example, with method = "ps"
, the glm
object containing the propensity score model will be included in the output.
Rearranged the help pages. Each method now has its own documentation page, linked from the weightit
help page.
Propensity scores are now included in the output for binary tretaments with gbm
and cbps
methods. Thanks to @Blanch-Font for the suggestion.
Other bug fixes and minor changes.
Version 0.4.0
Added trim()
function to trim weights.
Added ps.cont()
function, which estimates generalized propensity score weights for continuous treatments using generalized boosted modeling, as in twang
. This function uses the same syntax as ps()
in twang
, and can also be accessed using weightit()
with method = "gbm"
. Support functions were added to make it compatible with twang
functions for assessing balance (e.g., summary
, bal.table
, plot
). Thanks to Donna Coffman for enlightening me about this method and providing the code to implement it.
The input formula is now much more forgiving, allowing objects in the environment to be included. The data
argument to weightit()
is now optional. To simplify things, the output object no longer contains a data
field.
Under-the-hood changes to facilitate adding new features and debugging. Some aspects of the output objects have been slightly changed, but it shouldn't affect use for most users.
Fixed a bug where variables would be thrown out when method = "ebal"
.
Added support for sampling weights with stable balancing weighting and empirical balancing calibration weighting.
Version 0.3.2
Added new moments
and int
options for some weightit()
methods to easily specify moments and interactions of covariates.
Fixed bug when using objects not in the data set in weightit()
. Behavior has changed to include transformed covariates entered in formula in weightit()
output.
Fixed bug resulting from potentially colinearity when using ebal
or ebcw
.
Added a vignette.
Version 0.3.1
Edits to code and help files to protect against missing CBPS
package.
Corrected sampling weights functionality so they work correctly. Also expanded sampling weights to be able to be used with all methods, including those that do not natively allow for sampling weights (e.g., sbw
and ATE
)
Minor bug fixes and spelling corrections.
Version 0.3.0
Added weightitMSM()
function (and supporting print()
and summary()
functions) to estimate weights for marginal structural models with time-varying treatments and covariates.
Fixed some bugs, including when using CBPS with continuous treatments, and when using focal
incorrectly.
Version 0.2.0
Added method = "sbw"
for stable balancing weights
Allowed for estimation of multinomial propensity scores using multiple binary regressions if mlogit
is not installed
Allowed for estimation of multinomial CBPS using multiple binary CBPS for more than 4 groups
Added README and NEWS
Version 0.1.0