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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