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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>.
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| Author | Naeem Khoshnevis [aut] (<https://orcid.org/0000-0003-4315-1426>), Daniela Maria Garcia [aut] (<https://orcid.org/0000-0003-3226-3561>), Riccardo Cadei [aut] (<https://orcid.org/0000-0003-2416-8943>), Kwonsang Lee [aut] (<https://orcid.org/0000-0002-5823-4331>), Falco Joannes Bargagli Stoffi [aut, cre] (<https://orcid.org/0000-0002-6131-8165>) |
| Maintainer | Falco Joannes Bargagli Stoffi <fbargaglistoffi@hsph.harvard.edu> |
| License | GPL-3 |
| Version | 0.2.7 |
| URL | https://github.com/NSAPH-Software/CRE |
| Package repository | View on CRAN |
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