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.
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Release files for pycre 0.0.1
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Release files / pycre-0.0.1.tar.gz
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twine/4.0.2 CPython/3.10.12
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