calibrate {WeightIt}

Calibrate Propensity Score Weights

Description

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, ...)

## Default S3 method:
calibrate(x, treat, s.weights = NULL, data = NULL, ...)

## S3 method for class '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"))
summary(W)

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

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