Interpretable Discovery and Inference of Heterogeneous Treatment Effects R package CRE version 0.2.7 CRAN Task View: CausalInference (https://CRAN.R-project.org/view=CausalInference) 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>. Software 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) Falco Joannes Bargagli Stoffi <fbargaglistoffi@hsph.harvard.edu> Comprehensive R Archive Network (CRAN) Naeem Khoshnevis [aut] (ORCID: <https://orcid.org/0000-0003-4315-1426>), Daniela Maria Garcia [aut] (ORCID: <https://orcid.org/0000-0003-3226-3561>), Riccardo Cadei [aut] (ORCID: <https://orcid.org/0000-0003-2416-8943>), Kwonsang Lee [aut] (ORCID: <https://orcid.org/0000-0002-5823-4331>), Falco Joannes Bargagli Stoffi [aut, cre] (ORCID: <https://orcid.org/0000-0002-6131-8165>) GPL-3 2024-10-19 application/tgz https://CRAN.R-project.org/package=CRE doi:10.32614/CRAN.package.CRE en-US