Differentiable DAG Sampling

This repository presents the models of the paper:

Differentiable DAG Sampling
Bertrand Charpentier, Simon Kibler, Stephan Günnemann
International Conference on Learning Representations (ICLR), 2022.

[Paper|Video]

Requirements

To install requirements:

conda create -n differentiable-dag-sampling python=3.7
conda activate differentiable-dag-sampling
conda install --force-reinstall -y -q --name differentiable-dag-sampling -c conda-forge --file requirements-conda.txt
pip install -r requirements-pip.txt

To run notebooks:

conda install -c conda-forge jupyterlab
conda install -c anaconda ipykernel
python -m ipykernel install --user --name=differentiable-dag-sampling
python setup.py develop

To install R requirements (useful to load Sachs dataset):

cd /cppext
python setup.py install
R < install_requirements.R --no-save

Data

You can find the datasets at the following anonymous link.

Experiments

You can find a notebook to run DP-DAG and VI-DP-DAG in the folder src/notebooks.

Cite

Please cite our paper if you use the model or this code in your own work:

@incollection{dpdag,
title = {Differentiable DAG Sampling},
author = {Charpentier, Bertrand, Kibler, Simon and G\"{u}nnemann, Stephan},
booktitle = {International Conference on Learning Representations 10},
year = {2022},
}