Metadata-Version: 2.1
Name: pycre
Version: 0.0.1
Summary: Python implementation of Causal Rule Ensemble 
Home-page: https://github.com/NSAPH-Software/pycre
Author: ('Riccardo Cadei', 'Naeem Khoshnevis', 'Falco Joannes Bargagli Stoffi')
Author-email: rcadei@hsph.harvard.edu,nkhoshnevis@g.harvard.edu,fbargaglistoffi@hsph.harvard.edu
Maintainer: Naeem Khoshnevis
Maintainer-email: nkhoshnevis@g.harvard.edu
License: GPLv3
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

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 theoretical convergence 
        guarantees support it. Bargagli-Stoffi, F. J., Cadei, R., 
        Lee, K., & Dominici, F. (2023) Causal rule ensemble: 
        Interpretable Discovery and Inference of Heterogeneous 
        Treatment Effects. arXiv preprint <arXiv:2009.09036>.
