Plot Dual Variables for Assessing Balance Constraints

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. 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
#> Summary of weights:
#> 
#> - Weight ranges:
#>         Min                                  Max
#> treated   1                   ||          1.    
#> control   0 |---------------------------| 1.5946
#> 
#> - Units with 5 greatest weights by group:
#>                                            
#>               1      2      3      4      5
#>  treated      1      1      1      1      1
#>              79    118    127    156    164
#>  control 1.5946 1.5946 1.5946 1.5946 1.5946
#> 
#>            L2    L1 L∞ Rel Ent # Zeros
#> treated 0.    0.     0   0.          0
#> control 0.532 0.462  1   0.181       0
#> 
#> - Effective Sample Sizes:
#>            Control Treated
#> Unweighted  429.       185
#> Weighted    334.41     185
#> 
#              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
#> Summary of weights:
#> 
#> - Weight ranges:
#>         Min                                  Max
#> treated   1                  ||           1.    
#> control   0 |---------------------------| 1.7539
#> 
#> - Units with 5 greatest weights by group:
#>                                            
#>               1      2      3      4      5
#>  treated      1      1      1      1      1
#>             419    404    412    387    395
#>  control 1.7343 1.7441 1.7441 1.7539 1.7539
#> 
#>            L2    L1 L∞ Rel Ent # Zeros
#> treated 0.    0.     0   0.          0
#> control 0.534 0.465  1   0.183       0
#> 
#> - Effective Sample Sizes:
#>            Control Treated
#> Unweighted  429.       185
#> Weighted    333.86     185
#> 
#              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
#> Summary of weights:
#> 
#> - Weight ranges:
#>         Min                                  Max
#> treated   1                ||             1.    
#> control   0 |---------------------------| 1.8712
#> 
#> - Units with 5 greatest weights by group:
#>                                           
#>              1      2      3      4      5
#>  treated     1      1      1      1      1
#>            419    404    412    387    395
#>  control 1.857 1.8641 1.8641 1.8712 1.8712
#> 
#>            L2    L1 L∞ Rel Ent # Zeros
#> treated 0.    0.     0   0.          0
#> control 0.676 0.647  1   0.277       0
#> 
#> - Effective Sample Sizes:
#>            Control Treated
#> Unweighted  429.       185
#> Weighted    294.35     185
#> 
#              on married had a large effect on the
#              variability of the weights, shrinking
#              the ESS