Estimates average treatment effects using kernel energy balancing with random forest similarity kernels. A multivariate random forest jointly models covariates, outcome, and treatment to build a similarity kernel between observations. This kernel is then used for energy balancing to create weights that control for confounding. The method is described in De and Huling (2025) <doi:10.48550/arXiv.2512.18069>.
| Version: | 0.1.0 |
| Imports: | grf (≥ 2.3.0), MASS, Matrix, methods, Rcpp |
| LinkingTo: | Rcpp, RcppEigen |
| Suggests: | ggplot2, knitr, osqp, rmarkdown, testthat (≥ 3.0.0), WeightIt |
| Published: | 2026-04-07 |
| DOI: | 10.32614/CRAN.package.forestBalance (may not be active yet) |
| Author: | Jared Huling [aut, cre], Simion De [aut] |
| Maintainer: | Jared Huling <jaredhuling at gmail.com> |
| BugReports: | https://github.com/jaredhuling/forestBalance/issues |
| License: | GPL (≥ 3) |
| URL: | https://github.com/jaredhuling/forestBalance |
| NeedsCompilation: | yes |
| CRAN checks: | forestBalance results |
| Package source: | forestBalance_0.1.0.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available |
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