Provides a new method for interpretable heterogeneous treatment effects characterization in terms of decision rules via an extensive exploration of heterogeneity patterns by an ensemble-of-trees approach, enforcing high stability in the discovery. It relies on a two-stage pseudo-outcome regression, and it is supported by theoretical convergence guarantees. Bargagli-Stoffi, F. J., Cadei, R., Lee, K., & Dominici, F. (2023) Causal rule ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects. arXiv preprint <doi:10.48550/arXiv.2009.09036>.
Version: 0.2.7 Depends: R (≥ 3.5.0) Imports: MASS, stats, logger, gbm, randomForest, methods, xgboost, RRF, data.table, xtable, glmnet, bartCause, stabs, stringr, SuperLearner, magrittr, ggplot2, arules Suggests: grf, BART, gnm, covr, knitr, rmarkdown, testthat (≥ 3.0.0) Published: 2024-10-19 DOI: 10.32614/CRAN.package.CRE Author: Naeem Khoshnevis [aut], Daniela Maria Garcia [aut], Riccardo Cadei [aut], Kwonsang Lee [aut], Falco Joannes Bargagli Stoffi [aut, cre] Maintainer: Falco Joannes Bargagli Stoffi <fbargaglistoffi at hsph.harvard.edu> BugReports: https://github.com/NSAPH-Software/CRE/issues License: GPL-3 Copyright: Harvard University URL: https://github.com/NSAPH-Software/CRE NeedsCompilation: no Language: en-US Citation: CRE citation info Materials: README NEWS CRAN checks: CRE results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=CRE to link to this page.
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