Heteroscedastic Causal Structure Learning (HOST)

Setup • Usage • Experiments • Citation

This is the implementation of our paper: Bao Duong and Thin Nguyen. Heteroscedastic Causal Structure Learning. Accepted at the 26th European Conference on Artificial Intelligence (ECAI 2023).

Setup

conda env create -n host --file env_loose.yml # or env_strict.yml
conda activate host

Usage

from src.data_gen import simulate_cd
from src.methods.HOST import HOST

if __name__ == '__main__':
    data, dag_gt = simulate_cd(N=500, d=5, dag_type='ER', k=1, random_state=0)
    perm, dag = HOST(X=data, return_dag=True)

Experiments

For example, to run Figure 1 experiment:

python experiments/synthetic/perm.py --methods HOST --n_jobs=8

Experiment configuration can be set in experiments/*/config.yml, and result dataframes are stored in experiments/*/results/ after the command is finished.

Citation

@article{duong2023heteroscedastic,
      title={Heteroscedastic Causal Structure Learning}, 
      author={Bao Duong and Thin Nguyen},
      year={2023},
      journal={arXiv preprint arXiv:2307.07973}
}