Package: CRE 0.2.6

Naeem Khoshnevis

CRE: Interpretable Discovery and Inference of Heterogeneous Treatment Effects

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>.

Authors:Naeem Khoshnevis [aut, cre], Daniela Maria Garcia [aut], Riccardo Cadei [aut], Kwonsang Lee [aut], Falco Joannes Bargagli Stoffi [aut]

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Bug tracker:https://github.com/nsaph-software/cre/issues

On CRAN:

4 exports 13 stars 2.05 score 61 dependencies 12 scripts 1.1k downloads

Last updated 5 months agofrom:608e8e919d. Checks:OK: 7. Indexed: yes.

Exports:cregenerate_cre_datasetget_loggerset_logger

Dependencies:arulesbartCausebitopscaToolsclicodetoolscolorspacecvAUCdata.tabledbartsfansifarverforeachgamgbmgenericsggplot2glmnetgluegplotsgtablegtoolsisobanditeratorsjsonliteKernSmoothlabelinglatticelifecycleloggermagrittrMASSMatrixmgcvmunsellnlmennlspillarpkgconfigR6randomForestRColorBrewerRcppRcppEigenrlangROCRRRFscalesshapestabsstringistringrSuperLearnersurvivaltibbleutf8vctrsviridisLitewithrxgboostxtable