WeightIt

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.    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 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.    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.

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()`
-	Bayesian additive regression trees PS (`"bart"`)	`BART::gbart()`
-	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()`
-	Bayesian additive regression trees PS (`"bart"`)	`BART::mbart()`
-	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()`
-	Bayesian additive regression trees PS (`"bart"`)	`BART::gbart()`

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 noah.greifer@gmail.com. Fan mail is greatly appreciated.

bvegetabile/WeightIt

WeightIt