Code used to estimate conditional average treatment effect for the purpose of predictive enrichment in the paper published in Nature Communications by Falet et. al (2022) titled "Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning." This repository does not contain dataset-specific code, since the data we used is currently not publically available. The code we provide is meant to be provide a flexible starting point that can be adapted to suite a particular task.
An simple implementation that uses a randomly generated dataset is provided as an example in the main() function.
If you use part of this code, please cite our paper:
@article{falet2022estimating,
title={Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning},
author={Falet, Jean-Pierre R and Durso-Finley, Joshua and Nichyporuk, Brennan and Schroeter, Julien and Bovis, Francesca and Sormani, Maria-Pia and Precup, Doina and Arbel, Tal and Arnold, Douglas Lorne},
journal={Nature communications},
volume={13},
number={1},
pages={1--12},
year={2022},
publisher={Nature Publishing Group}
}