felixthoemmes/weightit

WeightIt: an R package for propensity score weighting

WeightIt: Weighting for Covariate Balance in Observational Studies

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Overview

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 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 ATE:

data("lalonde", package = "cobalt")

W <- weightit(treat ~ age + educ + nodegree + 
                married + race + re74 + re75, 
              data = lalonde, method = "glm", 
              estimand = "ATE")
W

#> A weightit object
#>  - method: "glm" (propensity score weighting with GLM)
#>  - 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 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  1.7569   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.    185.  
#> Adjusted    329.01   58.33

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 the 5 most extreme weights by group:
#>                                                 
#>               68     116      10     137     124
#>  treated 13.5451 15.9884 23.2967 23.3891 40.0773
#>              597     573     381     411     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   0.534       0
#> control       0.552 0.391   0.118       0
#> 
#> - Effective Sample Sizes:
#> 
#>            Control Treated
#> Unweighted  429.    185.  
#> Weighted    329.01   58.33

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. See vignette("installing-packages") for information on how to install these packages.

Treatment type	Method (method =)	Package
Binary	Binary regression PS ("glm")	various
-	Generalized boosted modeling PS ("gbm")	gbm
-	Covariate Balancing PS ("cbps")	CBPS
-	Non-Parametric Covariate Balancing PS ("npcbps")	CBPS
-	Entropy Balancing ("ebal")	-
-	Optimization-Based Weights ("optweight")	optweight
-	SuperLearner PS ("super")	SuperLearner
-	Bayesian additive regression trees PS ("bart")	dbarts
-	Energy Balancing ("energy")	-
Multinomial	Multinomial regression PS ("glm")	various
-	Generalized boosted modeling PS ("gbm")	gbm
-	Covariate Balancing PS ("cbps")	CBPS
-	Non-Parametric Covariate Balancing PS ("npcbps")	CBPS
-	Entropy Balancing ("ebal")	-
-	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")	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
-	Energy Balancing ("energy")	-

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 the author. Fan mail is greatly appreciated.