Causal Component Analysis is a project that bridges the gap between Independent Component Analysis (ICA) and Causal Representation Learning (CRL). This project includes implementations and experiments related to the papers:
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Liang, W., Kekić, A., von Kügelgen, J., Buchholz, S., Besserve, M., Gresele, L., & Schölkopf, B. (2023). Causal Component Analysis. In Proceedings of the Thirty-seventh Conference on Neural Information Processing Systems.
The corresponding experiments are in the experiments/cauca folder.
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von Kügelgen, J., Besserve, M., Liang, W., Gresele, L., Kekić, A., Bareinboim, E., Blei, D., & Schölkopf, B. (2023). Nonparametric Identifiability of Causal Representations from Unknown Interventions. In Proceedings of the Thirty-seventh Conference on Neural Information Processing Systems.
The corresponding experiments are in the experiments/nonparam_ident folder.
Clone the repository
git clone git@github.com:akekic/causal-component-analysis.git
and install the package
pip install -e .
This project is licensed under the MIT license. See the LICENSE file for details.
If you use CauCA
, please cite the
corresponding paper as follows.
Liang, W., Kekić, A., von Kügelgen, J., Buchholz, S., Besserve, M., Gresele, L., & Schölkopf, B. (2023). Causal Component Analysis. In Proceedings of the Thirty-seventh Conference on Neural Information Processing Systems.
Bibtex
@inproceedings{
liang2023causal,
title={Causal Component Analysis},
author={Wendong Liang and Armin Keki{\'c} and Julius von K{\"u}gelgen and Simon Buchholz and Michel Besserve and Luigi Gresele and Bernhard Sch{\"o}lkopf},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=HszLRiHyfO}
}