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, ATC, and other estimands 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.
For a complete vignette, see the CRAN
page for WeightIt
.
To install and load WeightIt
, use the code below:
#CRAN version
install.packages("WeightIt")
#Development version
devtools::install_github("ngreifer/WeightIt")
library("WeightIt")
The workhorse function of WeightIt
is weightit()
, which generates
weights from a given formula and data input according to methods and
other parameters specified by the user. Below is an example of the use
of weightit()
to generate propensity score 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")
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
- Weight statistics:
Coef of Var MAD Entropy # Zeros
treated 1.478 0.807 1.630 0
control 0.552 0.391 0.480 0
overall 1.371 0.591 1.024 0
- Effective Sample Sizes:
Control Treated
Unweighted 429.000 185.000
Weighted 329.008 58.327
Desirable qualities include small 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 |
---|---|---|
Binary | Binary regression PS ("ps" ) |
various |
- | Generalized boosted modeling PS ("gbm" ) |
gbm::gbm.fit() |
- | Covariate Balancing PS ("cbps" ) |
CBPS::CBPS() |
- | Non-Parametric Covariate Balancing PS ("npcbps" ) |
CBPS::npCBPS() |
- | Entropy Balancing ("ebal" ) |
ebal::ebalance() |
- | Empirical Balancing Calibration Weights ("ebcw" ) |
ATE::ATE() |
- | Optimization-Based Weights ("optweight" ) |
optweight::optweight() |
- | SuperLearner PS ("super" ) |
SuperLearner::SuperLearner() |
- | Energy Balancing ("energy" ) |
- |
Multinomial | Multinomial regression PS ("ps" ) |
various |
- | Generalized boosted modeling PS ("gbm" ) |
gbm::gbm.fit() |
- | Covariate Balancing PS ("cbps" ) |
CBPS::CBPS() |
- | Non-Parametric Covariate Balancing PS ("npcbps" ) |
CBPS::npCBPS() |
- | Entropy Balancing ("ebal" ) |
ebal::ebalance() |
- | Empirical Balancing Calibration Weights ("ebcw" ) |
ATE::ATE() |
- | Optimization-Based Weights ("optweight" ) |
optweight::optweight() |
- | SuperLearner PS ("super" ) |
SuperLearner::SuperLearner() |
- | Energy Balancing ("energy" ) |
- |
Continuous | Generalized linear model PS ("ps" ) |
glm() |
- | Generalized boosted modeling PS ("gbm" ) |
gbm::gbm.fit() |
- | Covariate Balancing PS ("cbps" ) |
CBPS::CBPS() |
- | Non-Parametric Covariate Balancing PS ("npcbps" ) |
CBPS::npCBPS() |
- | Entropy Balancing ("ebal" ) |
- |
- | Optimization-Based Weights ("optweight" ) |
optweight::optweight() |
- | SuperLearner PS ("super" ) |
SuperLearner::SuperLearner() |
In addition, WeightIt
implements the subgroup balancing propensity
score using the function sbps()
. Several other tools and utilities are
available.
Please submit bug reports or other issues to
https://github.com/ngreifer/WeightIt/issues. 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]. Fan mail is greatly appreciated.