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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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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