Use optimization to estimate weights that balance covariates for binary, multinomial, continuous, and longitudinal treatments in the spirit of Zubizarreta (2015)
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
solve_osqp() in the
rosqp 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
Below is an example of estimating weights with
optweight and assessing
balance on the covariates with
devtools::install_github("ngreifer/optweight") #development versionlibrary("optweight")library("cobalt")
data("lalonde")# Estimate weightsow <- optweight(treat ~ age + educ + race + nodegree + married + re74 + re75 +I(re74 == 0) + I(re75 == 0), data = lalonde, estimand = "ATT", tols = 0.01)ow
An 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 of weights: - Weight ranges: Min Max treated 1.0000 || 1.0000 control 0.0021 |---------------------------| 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
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 effectlibrary("jtools")summ(lm(re78 ~ treat, data = lalonde, weights = ow$weights), confint = TRUE,robust = TRUE, model.fit = FALSE)
MODEL INFO: Observations: 614 Dependent Variable: re78 Type: OLS linear regression Standard errors: Robust, type = HC3 Est. 2.5% 97.5% t val. p (Intercept) 5342.94 4635.09 6050.78 14.85 0.00 *** treat 1006.20 57.22 1955.19 2.09 0.04 *
The lower-level function
optweight.fit operates on the covariates and
treatment variables directly.
In addition to estimating balancing weights for estimating treatment
optweight can estimate sampling weights for generalizing an
estimate to a new target population defined by covariate moments using
optweight.svy and associated methods and functions for estimating survey weights using optimization. These weights when applied to the sample will yield a sample whose covariate means are equal (within a specified tolerance) to given target values.
Minor changes to
check.targets. It will now produce the covariate means when the
targets argument is empty and will produce the previous empty output, a named vector of
targets = NULL.
Changes to how dual variables are processed and displayed. Now, each dual variable coming from
optweight represents the change in the objective function corresponding to a 1-unit change in
tols. The reported duals are the sum of all the duals affected by the constraint, so you can now reliably predict the change in the objective function from a change in
tols (it was obscured and error-prone previously). The distinction between targeting duals and balance duals is maintained.