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
remotes::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
ATT:
data("lalonde", package = "cobalt")
W <- weightit(treat ~ age + educ + nodegree +
married + race + re74 + re75,
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:
fit <- lm_weightit(re78 ~ treat, data = lalonde,
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 tables below contains the available methods in WeightIt for
estimating weights for binary, multi-category, and continuous
treatments. Many of these methods do not require any other package to
use; see vignette("installing-packages") for information on how to
install packages that are used.
| Method | method |
|---|---|
| Binary regression PS | "glm" |
| Generalized boosted modeling PS | "gbm" |
| Covariate balancing PS | "cbps" |
| Non-Parametric covariate balancing PS | "npcbps" |
| Entropy balancing | "ebal" |
| Inverse probability tilting | "ipt" |
| Stable balancing weights | "optweight" |
| SuperLearner PS | "super" |
| Bayesian additive regression trees PS | "bart" |
| Energy balancing | "energy" |
| Method | method |
|---|---|
| Multinomial regression PS | "glm" |
| Generalized boosted modeling PS | "gbm" |
| Covariate balancing PS | "cbps" |
| Non-Parametric covariate balancing PS | "npcbps" |
| Entropy balancing | "ebal" |
| Inverse probability tilting | "ipt" |
| Stable balancing weights | "optweight" |
| SuperLearner PS | "super" |
| Bayesian additive regression trees PS | "bart" |
| Energy balancing | "energy" |
| Method | method |
|---|---|
| Generalized linear model GPS | "glm" |
| Generalized boosted modeling GPS | "gbm" |
| Covariate balancing GPS | "cbps" |
| Non-Parametric covariate balancing GPS | "npcbps" |
| Entropy balancing | "ebal" |
| Stable balancing weights | "optweight" |
| SuperLearner GPS | "super" |
| Bayesian additive regression trees GPS | "bart" |
| 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, calibrate()
to calibrate propensity scores, get_w_from_ps() to compute weights
from propensity scores.
WeightIt provides functions to fit weighted models that account for
the uncertainty in estimating the weights. These include
glm_weightit() for fitting generalized linear models,
ordinal_weightit() for ordinal regression models,
multinom_weightit() for multinomial regression models, and
coxph_weightit() for Cox proportional hazards models. Several methods
are available for computing the parameter variances, including
asymptotically correct M-estimation-base variances, robust variances
that treat the weights as fixed, and traditional and fractional weighted
bootstrap variances. Clustered variances are supported. See
vignette("estimating-effects") for information on how to use these
after weighting to estimate treatment effects.
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.