Propensity Score Matching — ps

Matches treated and control units based on estimated propensity scores. Fits a logistic regression model (or accepts a pre-fitted one), computes logit propensity scores, and calls match_couples() with a caliper on the logit scale.

Usage

ps_match(
  formula = NULL,
  data = NULL,
  treatment = NULL,
  ps_model = NULL,
  caliper_sd = 0.2,
  method = "auto",
  replace = FALSE,
  ratio = 1L,
  ...
)

Arguments

formula: Formula for propensity score model (treatment ~ covariates). Required if ps_model is NULL.
data: Combined dataset containing both treated and control units
treatment: Name of the binary treatment column (0/1 or logical)
ps_model: Pre-fitted glm object (alternative to formula). If provided, formula is ignored.
caliper_sd: Caliper width in standard deviations of logit(PS). Default: 0.2 (Rosenbaum and Rubin recommendation).
method: LAP solver method (default: "auto")
replace: If TRUE, match with replacement (default: FALSE)
ratio: Integer k for k:1 matching (default: 1)
...: Additional arguments passed to match_couples()

Value

A matching_result object with additional propensity score info in result$info$ps_model and result$info$caliper_value.

Details

The propensity score is the probability of treatment assignment conditional on observed covariates. Matching is performed on the logit of the propensity score (Rosenbaum and Rubin 1985), which provides better distributional properties than matching on the raw probability scale.

The default caliper of 0.2 SD of logit(PS) is recommended by Austin (2011) as removing approximately 98% of bias.

Examples

set.seed(42)
n <- 100
data <- data.frame(
  id = seq_len(n),
  treated = rbinom(n, 1, 0.4),
  age = rnorm(n, 50, 10),
  income = rnorm(n, 50000, 15000)
)
result <- ps_match(treated ~ age + income, data = data, treatment = "treated")
print(result)