This is a Pytorch implementation of the Learning Neural Causal Models from Unknown Interventions paper. Here we learn the causal model based on a meta-learning transfer objective from unknown intervention data. Please cite:
Nan Rosemary Ke*, Olexa Bilaniuk*, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio
@article{ke2019learning,
title={Learning Neural Causal Models from Unknown Interventions},
author={Ke, Nan Rosemary and Bilaniuk, Olexa and Goyal, Anirudh and Bauer, Stefan and Larochelle, Hugo and Pal, Chris and Bengio, Yoshua},
journal={arXiv preprint arXiv:1910.01075},
year={2019}
}
- This code is based on Pytorch. The conda enviroment for running this code can be installed as follows,
conda env create -f environment.yml
pip install -e .
CPU features required: AVX2, FMA (Intel Haswell+)
- Training code
# chain3
python run.py train --seed 1 --train_functional 10000 --mopt adam:5e-2,0.9 --gopt adam:5e-3,0.1 -v 500 --lsparse 0.1 --bs 256 --ldag 0.5 --predict 100 --temperature 1 --limit-samples 500 -N 2 -p chain3
# fork3
python run.py train --seed 1 --train_functional 10000 --mopt adam:5e-2,0.9 --gopt adam:5e-3,0.1 -v 500 --lsparse 0.1 --bs 256 --ldag 0.5 --predict 100 --temperature 1 --limit-samples 500 -N 2 -p fork3
# collider3
python run.py train --seed 1 --train_functional 10000 --mopt adam:5e-2,0.9 --gopt adam:5e-3,0.1 -v 500 --lsparse 0.1 --bs 256 --ldag 0.5 --predict 100 --temperature 1 --limit-samples 500 -N 2 -p collider3
# confounder3
python run.py train --seed 1 --train_functional 10000 --mopt adam:5e-2,0.9 --gopt adam:5e-3,0.1 -v 500 --lsparse 0.1 --bs 256 --ldag 0.5 --predict 100 --temperature 1 --limit-samples 500 -N 2 -p confounder3
--seed specifies the random seed
--mopt specifies the optimizer and learning rate used to train the functional parameters
--gopt specifies the optimizer and learning rate used to train the structural parameters
--predict specifies the number of samples used for predicting the intervened node. Specifying 0 for this argument uses the groundtruth intervention node.
--temperature specifies the temperature setting fot the softmax for the groundtruth structured causal model.
-N specifies the number of categories for the categorical distribution
-M specifies the number of discrete random variables
--graph allows one to specify via the command-line several causal DAG skeletons.
-p specifies, by name, one of several --graph presets for groundtruth causal graphs (e.g. chain3).
--train_functional specifies how many iterations to train the functional parameters.
--limit-samples specifies the number of samples used per intervention. Suggest to use 500 for graphs of size < 10 and 1000 for graphs size between 10 and 15.
By default, the models and log files are stored in the work directory.
- Extracting useful information from log files.
Strip all the SLURM output of interruptions:
scripts/strip_interrupts.py slurm-%j.out > STRIPPED_LOGFILE.txt
Extract gamma CE timeseries
scripts/series_gammace.py STRIPPED_LOGFILE.txt > gammace.txt
Compute AUROC-over-time
scripts/series_auc.py STRIPPED_LOGFILE_1.txt STRIPPED_LOGFILE_2.txt STRIPPED_LOGFILE_3.txt STRIPPED_LOGFILE_4.txt STRIPPED_LOGFILE_5.txt > auc.txt
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