ps_match: Propensity Score Matching

In couplr: Optimal Pairing and Matching via Linear Assignment

Propensity Score Matching

Description

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()

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.

Value

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

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)

couplr documentation built on March 3, 2026, 5:08 p.m.