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Calibrate Propensity Score Weights

Source: R/calibrate.R
calibrate.Rd

calibrate() performs Platt scaling to calibrate propensity scores as recommended by Gutman et al. (2024). This involves fitting a new propensity score model using logistic regression with the previously estimated propensity score as the sole predictor. Weights are computed using this new propensity score.

Usage

calibrate(x, ...)

# S3 method for default
calibrate(x, treat, s.weights = NULL, data = NULL, ...)

# S3 method for weightit
calibrate(x, ...)

Arguments

x

A weightit object or a vector of propensity scores. Only binary treatments are supported.

...

Not used.

treat

A vector of treatment status for each unit. Only binary treatments are supported.

s.weights

A vector of sampling weights or the name of a variable in data that contains sampling weights.

data

An optional data frame containing the variable named in s.weights when supplied as a string.

Value

If the input is a weightit object, the output will be a weightit object with the propensity scores replaced with the calibrated propensity scores and the weights replaced by weights computed from the calibrated propensity scores.

If the input is a numeric vector of weights, the output will be a numeric vector of the calibrated propensity scores.

References

Gutman, R., Karavani, E., & Shimoni, Y. (2024). Improving Inverse Probability Weighting by Post-calibrating Its Propensity Scores. Epidemiology, 35(4). doi:10.1097/EDE.0000000000001733

See also

weightit(), weightitMSM()

Examples

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

#Using GBM to estimate weights
(W <- weightit(treat ~ age + educ + married +
                 nodegree + re74, data = lalonde,
               method = "gbm", estimand = "ATT",
               criterion = "smd.max"))
#> A weightit object
#>  - method: "gbm" (propensity score weighting with GBM)
#>  - number of obs.: 614
#>  - sampling weights: none
#>  - treatment: 2-category
#>  - estimand: ATT (focal: 1)
#>  - covariates: age, educ, married, nodegree, re74
summary(W)
#>                   Summary of weights
#> 
#> - Weight ranges:
#> 
#>            Min                                   Max
#> treated 1.0000    ||                          1.0000
#> control 0.0038 |---------------------------| 10.6104
#> 
#> - Units with the 5 most extreme weights by group:
#>                                             
#>               5      4      3      2       1
#>  treated      1      1      1      1       1
#>             585    569    592    374     608
#>  control 4.3522 5.0089 5.8584 5.8584 10.6104
#> 
#> - Weight statistics:
#> 
#>         Coef of Var  MAD Entropy # Zeros
#> treated       0.000 0.00   0.000       0
#> control       2.422 1.06   1.016       0
#> 
#> - Effective Sample Sizes:
#> 
#>            Control Treated
#> Unweighted   429.      185
#> Weighted      62.6     185

#Calibrating the GBM propensity scores
Wc <- calibrate(W)

#Calibrating propensity scores directly
PSc <- calibrate(W$ps, treat = lalonde$treat)