couplr (version 1.1.0)

ps_match: 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,
  ...
)

Value

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

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.

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)