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https://github.com/piomonti/carefl

Code for "Causal autoregressive flows" - AISTATS, 2021
https://github.com/piomonti/carefl

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Code for "Causal autoregressive flows" - AISTATS, 2021

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# Code for "Causal Autoregressive Flow"



This repository contains code to run and reproduce experiments presented in [Causal Autoregressive Flows](https://arxiv.org/abs/2011.02268), presented at the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021).



The repository originally contained the code to reproduce results presented in [Autoregressive flow-based causal discovery and inference](https://arxiv.org/abs/2007.09390), presented at the 2nd ICML workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (2020).

Switch to the `workshop` branch to access this version of the code.



## Dependencies

This project was tested with the following versions:



- python 3.7

- numpy 1.18.2

- pytorch 1.4

- scikit-learn 0.22.2

- scipy 1.4.1

- matplotlib 3.2.1

- seaborn 0.10



This project uses normalizing flows implementation from this [repository](https://github.com/karpathy/pytorch-normalizing-flows).



## Usage



The `main.py` script is the main gateway to reproduce the experiments detailed in the mansucript, and is straightforward

to use. Type `python main.py -h` to learn about the options.



Hyperparameters can be changed through the configuration files under `configs/`.

The `main.py` is setup to read the corresponding config file for each experiment, but this can be overwritten using the

`-y` or `--config` flag.



The results are saved under the `run/` folder. This can be changed using the `--run` flag.



Running the `main.py` script will only produce data for a single set of parameters, which are specified in the config file.

These parameters include the dataset type, the number of simulations, the algorithm, the number of observations, the architectural parameters for the neural networks (number of layers, dimension of the hidden layer...), etc...



To reproduce the figures in the manuscript, the script should be run multiple time for each different combination of parameters, to generate the data used for the plots.

Convience scripts are provided to do this in parallel using SLURM (see below). These make use of certain debugging flags that overwrite certain fields in the config file.



Finally, the `flow.scale` field in the config files is used to switch from CAREFL to CAREFL-NS by setting it to `false`.



### Examples

Experiments where run using the SLURM system. The `slurm_main_cpu.sbatch` is used to run jobs on CPU, and

 `slurm_main.sbatch` for the GPU.



To run simulations in parallel:

```bash

for SIZE in 25 50 75 100 150 250 500; do

    for ALGO in lrhyv reci anm; do

        for DSET in linear hoyer2009 nueralnet_l1 mnm veryhighdim; do

            sbatch slurm_main_cpu.sbatch -s -m $DSET -a $ALGO -n $SIZE

        done

    done

done

ALGO=carefl

for SIZE in 25 50 75 100 150 250 500; do

    for DSET in linear hoyer2009 nueralnet_l1 mnm veryhighdim; do

        sbatch slurm_main_cpu.sbatch -s -m $DSET -a $ALGO -n $SIZE

    done

done



```



___

To run interventions:

```bash

for SIZE in 250 500 750 1000 1250 1500 2000 2500; do

    for ALGO in gp linear; do

        sbatch slurm_main_cpu.sbatch -i -a $ALGO -n $SIZE

    done

done

ALGO=carefl

for SIZE in 250 500 750 1000 1250 1500 2000 2500; do

    sbatch slurm_main_cpu.sbatch -i -a $ALGO -n $SIZE

done

```



___

To run arrow of time on EEG data:

```bash

for ALGO in LRHyv RECI ANM; do

    for IDX in {0..117}; do

        sbatch slurm_main_cpu.sbatch -e -n $IDX -a $ALGO --n-sims 11

    done

done

ALGO=carefl

for IDX in {0..117}; do

    sbatch slurm_main.sbatch -e -n $IDX -a $ALGO --n-sims 11

done

```



___

To run interventions on fMRI data (this experiment outputs to standard output):

```bash

python main.py -f

```



___

To run pairs:

```bash

for IDX in {1..108}; do

    sbatch slurm_main_cpu.sbatch -p -n $IDX --n-sims 10

done

```



## Reference



If you find this code helpful/inspiring for your research, we would be grateful if you cite the following:



```bib

@inproceedings{khemakhem2021causal,

  title = { Causal Autoregressive Flows },

  author = {Khemakhem, Ilyes and Monti, Ricardo and Leech, Robert and Hyvarinen, Aapo},

  booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics},

  pages = {3520--3528},

  year = {2021},

  editor = {Banerjee, Arindam and Fukumizu, Kenji},

  volume = {130},

  series = {Proceedings of Machine Learning Research},

  month = {13--15 Apr},

  publisher = {PMLR}

}

```



## License

A full copy of the license can be found [here](LICENSE).



```

MIT License



Copyright (c) 2020 Ilyes Khemakhem and Ricardo Pio Monti



Permission is hereby granted, free of charge, to any person obtaining a copy

of this software and associated documentation files (the "Software"), to deal

in the Software without restriction, including without limitation the rights

to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

copies of the Software, and to permit persons to whom the Software is

furnished to do so, subject to the following conditions:



The above copyright notice and this permission notice shall be included in all

copies or substantial portions of the Software.



THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

SOFTWARE.

```