WeightIt
is a one-stop package to generate balancing
weights for point and longitudinal treatments in observational studies.
Support is included for binary, multi-category, and continuous
treatments, a variety of estimands including the ATE, ATT, ATC, ATO, and
others, and support for a wide variety of weighting methods, including
those that rely on parametric modeling, machine learning, or
optimization. WeightIt
also provides functionality for
fitting regression models in weighted samples that account for
estimation of the weights in quantifying uncertainty.
WeightIt
uses a familiar formula interface and is meant to
complement MatchIt
as a package that provides a unified
interface to basic and advanced weighting methods.
For a complete vignette, see the website
for WeightIt
or vignette("WeightIt")
.
To install and load WeightIt
, use the code below:
#CRAN version
install.packages("WeightIt")
#Development version
::install_github("ngreifer/WeightIt")
remotes
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 ATT:
data("lalonde", package = "cobalt")
<- weightit(treat ~ age + educ + nodegree +
W + race + re74 + re75,
married data = lalonde, method = "glm",
estimand = "ATT")
W
#> A weightit object
#> - method: "glm" (propensity score weighting with GLM)
#> - number of obs.: 614
#> - sampling weights: none
#> - treatment: 2-category
#> - estimand: ATT (focal: 1)
#> - covariates: age, educ, nodegree, married, race, re74, re75
Evaluating weights has two components: evaluating the covariate
balance produced 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)
#> Balance Measures
#> Type Diff.Un Diff.Adj
#> prop.score Distance 1.7941 -0.0205
#> age Contin. -0.3094 0.1188
#> educ Contin. 0.0550 -0.0284
#> nodegree Binary 0.1114 0.0184
#> married Binary -0.3236 0.0186
#> race_black Binary 0.6404 -0.0022
#> race_hispan Binary -0.0827 0.0002
#> race_white Binary -0.5577 0.0021
#> re74 Contin. -0.7211 -0.0021
#> re75 Contin. -0.2903 0.0110
#>
#> Effective sample sizes
#> Control Treated
#> Unadjusted 429. 185
#> Adjusted 99.82 185
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 1.0000 || 1.0000
#> control 0.0092 |---------------------------| 3.7432
#>
#> - Units with the 5 most extreme weights by group:
#>
#> 5 4 3 2 1
#> treated 1 1 1 1 1
#> 597 573 381 411 303
#> control 3.0301 3.0592 3.2397 3.5231 3.7432
#>
#> - Weight statistics:
#>
#> Coef of Var MAD Entropy # Zeros
#> treated 0.000 0.000 0.000 0
#> control 1.818 1.289 1.098 0
#>
#> - Effective Sample Sizes:
#>
#> Control Treated
#> Unweighted 429. 185
#> Weighted 99.82 185
Desirable qualities include small coefficients of variation close to 0 and large effective sample sizes.
Finally, we can estimate the effect of the treatment using a weighted outcome model, accounting for estimation of the weights in the standard error of the effect estimate:
<- lm_weightit(re78 ~ treat, data = lalonde,
fit weightit = W)
summary(fit, ci = TRUE)
#>
#> Call:
#> lm_weightit(formula = re78 ~ treat, data = lalonde, weightit = W)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|) 2.5 % 97.5 %
#> (Intercept) 5135 583.8 8.797 1.411e-18 3990.9 6279
#> treat 1214 798.2 1.521 1.282e-01 -350.3 2778
#> Standard error: HC0 robust (adjusted for estimation of weights)
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. Many of these methods do not require any other package
to use (i.e., those with “-” in the Package column). See
vignette("installing-packages")
for information on how to
install packages that are used.
Treatment type | Method (method = ) |
Package |
---|---|---|
Binary | Binary regression PS ("glm" ) |
various |
- | Generalized boosted modeling PS ("gbm" ) |
gbm |
- | Covariate balancing PS ("cbps" ) |
- |
- | Non-parametric covariate balancing PS ("npcbps" ) |
CBPS |
- | Entropy Balancing ("ebal" ) |
- |
- | Inverse probability tilting ("ipt" ) |
- |
- | Optimization-based Weights ("optweight" ) |
optweight |
- | SuperLearner PS ("super" ) |
SuperLearner |
- | Bayesian additive regression trees PS ("bart" ) |
dbarts |
- | Energy balancing ("energy" ) |
- |
Multi-category | Multinomial regression PS ("glm" ) |
various |
- | Generalized boosted modeling PS ("gbm" ) |
gbm |
- | Covariate balancing PS ("cbps" ) |
- |
- | Non-Parametric covariate balancing PS ("npcbps" ) |
CBPS |
- | Entropy balancing ("ebal" ) |
- |
- | Inverse probability tilting ("ipt" ) |
- |
- | Optimization-based weights ("optweight" ) |
optweight |
- | SuperLearner PS ("super" ) |
SuperLearner |
- | Bayesian additive regression trees PS ("bart" ) |
dbarts |
- | Energy balancing ("energy" ) |
- |
Continuous | Generalized linear model GPS ("glm" ) |
- |
- | Generalized boosted modeling GPS ("gbm" ) |
gbm |
- | Covariate balancing GPS ("cbps" ) |
- |
- | Non-Parametric covariate balancing GPS ("npcbps" ) |
CBPS |
- | Entropy balancing ("ebal" ) |
- |
- | Optimization-based weights ("optweight" ) |
optweight |
- | SuperLearner GPS ("super" ) |
SuperLearner |
- | Bayesian additive regression trees GPS ("bart" ) |
dbarts |
- | Distance covariance optimal weighting ("energy" ) |
- |
In addition, WeightIt
implements the subgroup balancing
propensity score using the function sbps()
. Several other
tools and utilities are available, including trim()
to trim
or truncate weights.
Please submit bug reports, questions, comments, or other issues to https://github.com/ngreifer/WeightIt/issues. If you
would like to see your package or method integrated into
WeightIt
, please contact the author. Fan mail is greatly
appreciated.