Skip to contents

Estimate Propensity Scores

Source: R/propensity.R
estimate_propensity.Rd

Estimates P(T = 1 | X) using the specified model, with built-in cross-fitting support.

Usage

estimate_propensity(
  treatment,
  covariates,
  method = c("logistic", "ranger", "xgboost"),
  cross_fit = TRUE,
  n_folds = 5L,
  trim = 0.01,
  seed = NULL
)

Arguments

treatment

Binary vector (0/1). Treatment indicator.

covariates

Numeric matrix or data.frame. Confounders.

method

Character. "logistic" (default), "ranger", or "xgboost".

cross_fit

Logical. If TRUE, uses 5-fold cross-fitting to produce out-of-sample propensity estimates. Default is TRUE.

n_folds

Integer. Number of cross-fitting folds. Default is 5.

trim

Numeric. Trim extreme propensity scores to [trim, 1-trim]. Default is 0.01.

seed

Integer or NULL. Random seed for the cross-fitting fold assignment, so a fixed seed makes cross-fitted scores reproducible.

Value

A list of class "propensity_fit" with components:

scores

Estimated propensity scores P(T=1|X).

method

Method used.

trim

Trimming threshold applied.

n_trimmed

Number of scores that were trimmed.

See also

Other density ratio and propensity: assess_overlap(), effective_sample_size(), estimate_density_ratio(), fit_density_ratio(), plot_weights(), predict_density_ratio()

Examples

set.seed(42)
n <- 300
x <- matrix(rnorm(n * 3), n, 3)
logit_p <- 0.5 * x[, 1] - 0.3 * x[, 2]
t <- rbinom(n, 1, plogis(logit_p))
ps <- estimate_propensity(t, x)
summary(ps$scores)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  0.2537  0.4428  0.5121  0.5171  0.5989  0.7847