Interpretable Discovery and Inference of Heterogeneous Treatment Effects In health and social sciences, it is critically important to identify subgroups of the study population where a treatment has notable heterogeneity in the causal effects with respect to the average treatment effect (ATE). The bulk of heterogeneous treatment effect (HTE) literature focuses on two major tasks: (i) estimating HTEs by examining the conditional average treatment effect (CATE); (ii) discovering subgroups of a population characterized by HTE.
Several methodologies have been proposed for both tasks, but providing interpretability in the results is still an open challenge. Bargagli-Stoffi et al. (2023) proposed Causal Rule Ensemble, a new method for HTE characterization in terms of decision rules, via an extensive exploration of heterogeneity patterns by an ensemble-of-trees approach, enforcing stability in the discovery. pycre is a Python Package providing a flexible implementation of the Causal Rule Ensemble algorithm.
pip install pycreThe package is compatible with Python 3.6+. The full list of dependencies is reported in the file requirements.txt.
from pycre.cre import CRE
from pycre.dataset import dataset_generator
# generate synthetic dataset
X, y, z, _ = dataset_generator()
# define model and train
model = CRE()
model.fit(X, y, z)
# visualize
model.plot()
# predict
ite_pred = model.eval(X)More exhaustive examples and simulations are reported in the .ipynb files in the folder /notebooks.
Causal Rule Ensemble (methodological paper)
@article{bargagli2023causal,
title={Causal rule ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects},
author={Bargagli-Stoffi, Falco J and Cadei, Riccardo and Lee, Kwonsang and Dominici, Francesca},
journal={arXiv preprint arXiv:2009.09036},
year={2023}
}