AugBalWeight: Augmented Balancing Weights as Linear Regression

AugBalWeight implements the methodology from the Journal of the Royal Statistical Society Series B paper: > Bruns-Smith, D., Dukes, O., Feller, A., & Ogburn, E. L. (2026). Augmented balancing weights as linear regression. Journal of the Royal Statistical Society Series B: Statistical Methodology, 88(3), 699–723. https://doi.org/10.1093/jrsssb/qkaf019.

Key Features

• Numerical Equivalence: Computes implied single linear regression coefficients \(\hat{\beta}_{\text{aug}}\) combining outcome model and balancing weight parameters.
• Double Ridge (\(\ell_2\) Balancing): Smooth decay regularization path matching ridge regression shrinkage.
• Double Lasso (\(\ell_\infty\) Balancing): Soft-thresholding feature shift interpolation with double selection \(I_{\text{aug}} = I_\lambda \cup I_\delta\).
• Hyperparameter Tuning: Flexible cross-validation algorithms for outcome model (\(\lambda\)), covariate balance (\(\delta\)), and Riesz loss.
• Causal Estimands: Built-in wrappers for ATE, ATT, and ATC with robust influence-function-based variance estimation.

Installation

# Install locally from source package:
install.packages("AugBalWeight_0.1.0.tar.gz", repos = NULL, type = "source")

Quick Example

library(AugBalWeight)

# Load canonical LaLonde dataset
data(lalonde_data)

# Estimate ATT (Average Treatment Effect on the Treated)
fit_att <- aug_bal_att(
  Y = lalonde_data$re78,
  Z = lalonde_data$treat,
  X = lalonde_data[, c("age", "educ", "black", "hisp", "married", "re74", "re75")],
  type = "l2"
)

summary(fit_att)
plot(fit_att)

Citation

@article{bruns2026augmented,
  title={Augmented balancing weights as linear regression},
  author={Bruns-Smith, David and Dukes, Oliver and Feller, Avi and Ogburn, Elizabeth L},
  journal={Journal of the Royal Statistical Society Series B: Statistical Methodology},
  volume={88},
  number={3},
  pages={699--723},
  year={2026},
  publisher={Oxford University Press},
  doi={10.1093/jrsssb/qkaf019}
}