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AugBalWeight  

Augmented Balancing Weights as Linear Regression
View on CRAN: Click here


Download and install AugBalWeight package within the R console
Install from CRAN:
install.packages("AugBalWeight")

Install from Github:
library("remotes")
install_github("cran/AugBalWeight")

Install by package version:
library("remotes")
install_version("AugBalWeight", "0.1.0")



Attach the package and use:
library("AugBalWeight")
Maintained by
Shikhar Tyagi
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-08-21
Latest Update: 2026-08-21
Description:
Implements augmented balancing weights for causal inference and linear functional estimation based on David Bruns-Smith, Oliver Dukes, Avi Feller, and Elizabeth L. Ogburn (2026) <doi:10.1093/jrsssb/qkaf019>. Establishes numerical equivalence between augmented balancing weight estimators and single linear models with weighted regression coefficients. Provides flexible routines for double ridge (l2 balancing), double lasso (l-infinity balancing), and generalized augmented linear outcome models. Features cross-validation procedures for tuning outcome penalty parameters, covariate balance, and Riesz loss. Supports robust influence-function-based standard errors, bootstrap confidence intervals, balance diagnostic tools, and counterfactual prediction for treatment effects such as average treatment effect (ATE) and average treatment effect on the treated (ATT), expanding upon the doubly robust estimation framework established by Robins, Rotnitzky, and Zhao (1994) <doi:10.1080/01621459.1994.10476818> and Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, and Robins (2018) <doi:10.1111/ectj.12097>.
How to cite:
Shikhar Tyagi (2026). AugBalWeight: Augmented Balancing Weights as Linear Regression. R package version 0.1.0, https://cran.r-project.org/web/packages/AugBalWeight. Accessed 04 Oct. 2026.
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