plot.optweight: Plot Dual Variables for Assessing Balance Constraints

In optweight: Optimization-Based Stable Balancing Weights

Plot Dual Variables for Assessing Balance Constraints

Description

Plots the dual variables resulting from optweight() in a way similar to figure 2 of Zubizarreta (2015), which explained how to interpret these values. These represent the cost of changing the constraint on the variance of the resulting weights. For covariates with large values of the dual variable, tightening the constraint will increase the variability of the weights, and loosening the constraint will decrease the variability of the weights, both to a greater extent than would doing the same for covariate with small values of the dual variable.

Usage

## S3 method for class 'optweight'
plot(x, ...)

## S3 method for class 'optweightMV'
plot(x, which.treat = 1, ...)

## S3 method for class 'optweight.svy'
plot(x, ...)

Arguments

x	an optweight, optweightMV, or optweight.svy object; the output of a call to optweight(), optweightMV(), or optweight.svy().
...	Ignored.
which.treat	For optweightMV objects, which treatment to display. Only one may be displayed at a time.

Value

A ggplot object that can be used with other ggplot2 functions.

References

Zubizarreta, J. R. (2015). Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data. Journal of the American Statistical Association, 110(511), 910–922. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.2015.1023805")}

See Also

optweight(), optweightMV(), or optweight.svy() to estimate the weights and the dual variables

plot.summary.optweight() for plots of the distribution of weights

Examples

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

tols <- process_tols(treat ~ age + educ + married +
                       nodegree + re74, data = lalonde,
                     tols = .1)

#Balancing covariates between treatment groups (binary)
ow1 <- optweight(treat ~ age + educ + married +
                   nodegree + re74, data = lalonde,
                 tols = tols,
                 estimand = "ATT")

summary(ow1) # Note the RMSE Dev and effective
#              sample size (ESS)

plot(ow1) # age has a low value, married is high

tols["age"] <- 0
ow2 <- optweight(treat ~ age + educ + married +
                   nodegree + re74, data = lalonde,
                 tols = tols,
                 estimand = "ATT")

summary(ow2) # Notice that tightening the constraint
#              on age had a negligible effect on the
#              variability of the weights and ESS

tols["age"] <- .1
tols["married"] <- 0
ow3 <- optweight(treat ~ age + educ + married +
                   nodegree + re74, data = lalonde,
                 tols = tols,
                 estimand = "ATT")

summary(ow3) # In contrast, tightening the constraint
#              on married had a large effect on the
#              variability of the weights, shrinking
#              the ESS

optweight documentation built on Sept. 9, 2025, 5:42 p.m.