CERFIT: Causal Effect Random Forest of Interaction Tress

Fits a Causal Effect Random Forest of Interaction Tress (CERFIT) which is a modification of the Random Forest algorithm where each split is chosen to maximize subgroup treatment heterogeneity. Doing this allows it to estimate the individualized treatment effect for each observation in either randomized controlled trial (RCT) or observational data. For more information see X. Su, A. T. Peña, L. Liu, and R. A. Levine (2018) <doi:10.48550/arXiv.1709.04862>.

Version: 0.1.0
Depends: R (≥ 2.10)
Imports: partykit, CBPS, randomForest, twang, Rcpp, stats, glmnet
LinkingTo: Rcpp, RcppArmadillo
Published: 2022-06-01
Author: Justin Thorp [aut, cre], Luo Li [aut], Juanjuan Fan [aut]
Maintainer: Justin Thorp <jjtthorp at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: README
CRAN checks: CERFIT results


Reference manual: CERFIT.pdf


Package source: CERFIT_0.1.0.tar.gz
Windows binaries: r-devel: CERFIT_0.1.0.zip, r-release: CERFIT_0.1.0.zip, r-oldrel: CERFIT_0.1.0.zip
macOS binaries: r-release (arm64): CERFIT_0.1.0.tgz, r-oldrel (arm64): CERFIT_0.1.0.tgz, r-release (x86_64): CERFIT_0.1.0.tgz, r-oldrel (x86_64): CERFIT_0.1.0.tgz


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