CRE: Interpretable Discovery and Inference of Heterogeneous Treatment Effects (original) (raw)
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 |
| 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 |
| In views: | CausalInference |
| CRAN checks: | CRE results |
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