Scalable framework for learning causal structure from two observational regimes with unknown soft intervention targets that generalizes to out-of-distribution graphs and causal mechanisms.
You need to have Python=3.10 or newer installed on your system. If you don't have Python installed, we recommend installing Mambaforge.
PyPI package coming soon!
Install the latest development version via the following command:
pip install git+https://github.com/azizilab/scone.git@mainTutorials and usage examples coming soon.
If you find our work useful please cite our preprint: https://arxiv.org/abs/2603.03411v1
Scalable Contrastive Causal Discovery under Unknown Soft Interventions
@article{zhang2026scalable,
title={Scalable Contrastive Causal Discovery under Unknown Soft Interventions},
author={Zhang, Mingxuan and Desai, Khushi and Kevlishvili, Sopho and Azizi, Elham},
journal={arXiv preprint arXiv:2603.03411},
year={2026}
}
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Mingxuan Zhang, Khushi Desai, Sopho Kevlishvili and Elham Azizi are inventors on a provisional patent application, filed on March 4, 2026, by The Trustees of Columbia University in the City of New York directed to the subject matter of the manuscript associated with this repository.