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Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence [ICML 2025]

Installation

pip install -e .

Dependencies

econml
scikit-learn
catenets
scipy
tqdm
tabpfn

Repository Structure

permucate/    # Core methods and main implementation
tests/        # Unit tests for core methods
scripts/      # Python scripts to reproduce the paper's experiments

Citation

If you use this code in your research, please cite our paper:

@inproceedings{
    paillard2025measuring,
    title={Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence},
    author={Joseph Paillard and Angel David REYERO LOBO and Vitaliy Kolodyazhniy and Bertrand Thirion and Denis-Alexander Engemann},
    booktitle={Forty-second International Conference on Machine Learning},
    year={2025},
    url={https://openreview.net/forum?id=HlRUGC5ork}
}

Contact

For any questions, please open an issue on this repository or contact us at joseph.paillard@roche.com.

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Code for Measuring Variable Importance in Heterogeneous Treatment Effects

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