CRE: Interpretable Subgroups Identification Through Ensemble Learning of Causal Rules

Provides an interpretable identification of subgroups with heterogeneous causal effect. The heterogeneous subgroups are discovered through ensemble learning of causal rules. Causal rules are highly interpretable if-then statement that recursively partition the features space into heterogeneous subgroups. A small number of significant causal rules are selected through Stability Selection to control for family-wise error rate in the finite sample setting. It proposes various estimation methods for the conditional causal effects for each discovered causal rule. It is highly flexible and multiple causal estimands and imputation methods are implemented. Lee, K., Bargagli-Stoffi, F. J., & Dominici, F. (2020). Causal rule ensemble: Interpretable inference of heterogeneous treatment effects. arXiv preprint <arXiv:2009.09036>.

Version: 0.1.1
Depends: R (≥ 3.5.0)
Imports: MASS, stats, logger, gbm, randomForest, methods, xgboost, RRF, data.table, xtable, glmnet, bartCause, stabs, stringr, SuperLearner, dplyr, magrittr, ggplot2, bcf, inTrees
Suggests: baggr, grf, BART, gnm, covr, knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2022-10-22
Author: Naeem Khoshnevis ORCID iD [aut, cre] (FASRC), Daniela Maria Garcia ORCID iD [aut], Riccardo Cadei ORCID iD [aut], Kwonsang Lee ORCID iD [aut], Falco Joannes Bargagli Stoffi ORCID iD [aut]
Maintainer: Naeem Khoshnevis <nkhoshnevis at>
License: GPL-3
Copyright: Harvard University
NeedsCompilation: no
Language: en-US
Materials: README NEWS
CRAN checks: CRE results


Reference manual: CRE.pdf
Vignettes: CRE


Package source: CRE_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): CRE_0.1.1.tgz, r-release (x86_64): CRE_0.1.1.tgz, r-oldrel (x86_64): CRE_0.1.1.tgz
Old sources: CRE archive


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