A Toolkit for Calculating and Working with Propensity Scores.

propensity

Overview

propensity makes it easy to calculate propensity score weights and use them to estimate causal effects. It supports:

• Six estimands for binary exposures (ATE, ATT, ATU, ATO, ATM, and entropy weights)
• Binary, categorical, and continuous exposures
• Trimming, truncation, and calibration for extreme propensity scores
• Inverse probability weighted estimation with standard errors that account for propensity score estimation

You can learn more in vignette("propensity").

Installation

You can install propensity from CRAN with:

install.packages("propensity")

You can install the development version of propensity from GitHub with:

# install.packages("pak")
pak::pak("r-causal/propensity")

Usage

library(propensity)

# Simulate data with a confounder, binary exposure, and binary outcome
n <- 200
x1 <- rnorm(n)
z <- rbinom(n, 1, plogis(0.5 * x1))
y <- rbinom(n, 1, plogis(-0.5 + 0.8 * z + 0.3 * x1))
dat <- data.frame(x1, z, y)

# Step 1: Fit a propensity score model
ps_mod <- glm(z ~ x1, data = dat, family = binomial())

# Step 2: Calculate ATE weights and fit a weighted outcome model
wts <- wt_ate(ps_mod)
outcome_mod <- glm(y ~ z, data = dat, family = binomial(), weights = wts)

# Step 3: Estimate causal effects with correct standard errors
ipw(ps_mod, outcome_mod)
#> Inverse Probability Weight Estimator
#> Estimand: ATE 
#> 
#> Propensity Score Model:
#>   Call: glm(formula = z ~ x1, family = binomial(), data = dat) 
#> 
#> Outcome Model:
#>   Call: glm(formula = y ~ z, family = binomial(), data = dat, weights = wts) 
#> 
#> Estimates:
#>         estimate std.err       z ci.lower ci.upper conf.level   p.value    
#> rd       0.14230 0.07038 2.02194   0.0044  0.28025       0.95 0.0431831 *  
#> log(rr)  0.28031 0.10770 2.60262   0.0692  0.49141       0.95 0.0092513 ** 
#> log(or)  0.57339 0.16200 3.53950   0.2559  0.89090       0.95 0.0004009 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ipw() uses linearization to account for uncertainty in the estimated propensity scores when computing standard errors.

Estimands

Each weight function targets a different population:

ATO and ATM weights are bounded by construction, making them a good alternative when ATE weights are highly variable.

Learn more

• Causal Inference in R – A book on causal inference methods in R
• vignette("propensity") – Getting started with propensity score weighting
• propensity package documentation – Full reference and articles.