➡️The API doc is available here⬅️
This repository contains code associated with the FedECA arXiv paper.
Please cite our paper if you use our code !
Before using the code be sure to check our license first.
To install the package, create an env with python 3.9 with conda
conda create -n fedeca python=3.9
conda activate fedecaWithin the environment, install the package by running:
git clone https://github.com/owkin/fedeca.git
cd fedeca
pip install -e ".[all_extra]"
If you plan on developing, you should also install the pre-commit hooks
pre-commit installThis will run all the pre-commit hooks at each commit, ensuring a clean repo.
Go here.
@ARTICLE{terrail2023fedeca,
author = {{Ogier du Terrail}, Jean and {Klopfenstein}, Quentin and {Li}, Honghao and {Mayer}, Imke and {Loiseau}, Nicolas and {Hallal}, Mohammad and {Debouver}, Michael and {Camalon}, Thibault and {Fouqueray}, Thibault and {Arellano Castro}, Jorge and {Yanes}, Zahia and {Dahan}, Laetitia and {Ta{\"\i}eb}, Julien and {Laurent-Puig}, Pierre and {Bachet}, Jean-Baptiste and {Zhao}, Shulin and {Nicolle}, Remy and {Cros}, J{\'e}rome and {Gonzalez}, Daniel and {Carreras-Torres}, Robert and {Garcia Velasco}, Adelaida and {Abdilleh}, Kawther and {Doss}, Sudheer and {Balazard}, F{\'e}lix and {Andreux}, Mathieu},
title = "{FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings}",
journal = {arXiv e-prints},
keywords = {Statistics - Methodology, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning},
year = 2023,
month = nov,
eid = {arXiv:2311.16984},
pages = {arXiv:2311.16984},
doi = {10.48550/arXiv.2311.16984},
archivePrefix = {arXiv},
eprint = {2311.16984},
primaryClass = {stat.ME},
adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv231116984O},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}