optweight contains functions to estimate weights that balance
treatments to given balance thresholds. It solves a quadratic
programming problem to minimize an objective function of the weights
using solve_osqp() in the osqp package. This is the method described
in Zubizarreta (2015). optweight extends the method to multinomial,
continuous, and longitudinal treatments and provides a simple user
interface and compatibility with the cobalt package.
Below is an example of estimating weights with optweight and assessing
balance on the covariates with cobalt.
devtools::install_github("ngreifer/optweight") #development version
library("optweight")
library("cobalt")data("lalonde")
#Estimate weights
ow <- optweight(treat ~ age + educ + race + nodegree + married +
re74 + re75 + I(re74 == 0) + I(re75 == 0),
data = lalonde, estimand = "ATT", tols = .01)
owAn optweight object
- number of obs.: 614
- sampling weights: none
- treatment: 2-category
- estimand: ATT (focal: 1)
- covariates: age, educ, race, nodegree, married, re74, re75, I(re74 == 0), I(re75 == 0)
summary(ow)Summary of weights:
- Weight ranges:
Min Max
treated 1 || 1.0000
control 0 |---------------------------| 7.4319
- Units with 5 greatest weights by group:
2 3 4 5 6
treated 1 1 1 1 1
608 574 559 573 303
control 7.2344 7.3161 7.4058 7.4058 7.4319
Coef of Var Mean Abs Dev
treated 0.0000 0.0000
control 1.9019 1.3719
overall 1.5897 0.9585
- Effective Sample Sizes:
Control Treated
Unweighted 429.000 185
Weighted 92.917 185
bal.tab(ow)Call
optweight(formula = treat ~ age + educ + race + nodegree + married +
re74 + re75 + I(re74 == 0) + I(re75 == 0), data = lalonde,
tols = 0.01, estimand = "ATT")
Balance Measures
Type Diff.Adj
age Contin. 0.01
educ Contin. 0.01
race_black Binary 0.01
race_hispan Binary 0.00
race_white Binary -0.01
nodegree Binary 0.01
married Binary -0.01
re74 Contin. 0.01
re75 Contin. 0.01
I(re74 == 0) Binary 0.01
I(re75 == 0) Binary 0.01
Effective sample sizes
Control Treated
Unadjusted 429.000 185
Adjusted 92.917 185
#Estimate a treatment effect
library("jtools")
summ(lm(re78 ~ treat, data = lalonde, weights = ow$weights),
confint = TRUE, robust = TRUE, model.fit = FALSE,
model.info = FALSE)|
Est. |
2.5% |
97.5% |
t val. |
p |
|
|---|---|---|---|---|---|
|
(Intercept) |
5342.94 |
4058.75 |
6627.13 |
8.17 |
0.00 |
|
treat |
1006.20 |
-710.34 |
2722.74 |
1.15 |
0.25 |
|
Standard errors: Robust, type = HC3 |
The lower-level function optweight.fit operates on the covariates and
treatment variables directly.
In addition to estimating balancing weights for estimating treatment
effects, optweight can estimate sampling weights for generalizing an
estimate to a new target population defined by covariate moments using
the function optweight.svy.