ebal

Entropy balancing for the construction of balanced samples in observational studies. Reweights a control group so that user-specified covariate moments match those of a treatment group, or reweights a survey sample to match known population characteristics. The weights can then be passed to regression or other downstream models to estimate treatment effects on the reweighted data.

The method is described in:

Hainmueller, J. (2012). "Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies." Political Analysis, 20(1), 25–46. doi:10.1093/pan/mpr025

Installation

# From CRAN (when published)
install.packages("ebal")

# Development version from GitHub
# install.packages("remotes")
remotes::install_github("j-hai/ebal")

Quick start

library(ebal)

# Toy data
set.seed(1)
treatment <- c(rep(0, 50), rep(1, 30))
X <- rbind(replicate(3, rnorm(50, 0)), replicate(3, rnorm(30, 0.5)))
colnames(X) <- paste0("x", 1:3)
df <- data.frame(treat = treatment, X)

# Fit (formula interface)
fit <- ebalance(treat ~ x1 + x2 + x3, data = df)

# Or matrix interface
fit <- ebalance(Treatment = treatment, X = X)

print(fit)
summary(fit)        # balance table, before vs. after weighting
plot(fit)           # Love plot of standardized differences

# Use weights in a downstream regression
df$w <- weights(fit)        # length nrow(df), 1 for treated, eb-weight for controls
mod <- lm(y ~ treat, data = df, weights = w)   # if you have an outcome y

Trimming extreme weights

# Auto-minimize maximum weight ratio
trimmed <- ebalance.trim(fit)

# Or specify a target
trimmed <- ebalance.trim(fit, max.weight = 5)

trimmed$trim.feasible      # TRUE if target was met (or auto mode finished)
weights(trimmed)           # length-n vector for downstream use

If the requested max.weight is infeasible the function emits a warning and returns the most recent feasible fit with trim.feasible = FALSE.

What's new in 0.2.0

• Formula interface: ebalance(treat ~ x1 + x2, data = df)
• print(), summary(), plot(), weights() S3 methods
• New trim.feasible field on ebalance.trim objects
• Numerical hardening: no more NaN crashes on aggressive trim targets; graceful fallback when the inner solve becomes singular

See NEWS.md for the full change log.

License

GPL (>= 2). See LICENSE.note.