https://github.com/networks-learning/counterfactual-continuous-mdp

Code for "Finding Counterfactually Optimal Action Sequences in Continuous State Spaces", NeurIPS 2023.
https://github.com/networks-learning/counterfactual-continuous-mdp

causality counterfactuals decision-making decision-making-under-uncertainty explainable-ai explainable-ml machine-learning markov-decision-processes

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Code for "Finding Counterfactually Optimal Action Sequences in Continuous State Spaces", NeurIPS 2023.

• Host: GitHub
• URL: https://github.com/networks-learning/counterfactual-continuous-mdp
• Owner: Networks-Learning
• Created: 2023-05-31T19:48:56.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-18T06:59:45.000Z (about 1 year ago)
• Last Synced: 2024-04-16T02:14:54.101Z (7 months ago)
• Topics: causality, counterfactuals, decision-making, decision-making-under-uncertainty, explainable-ai, explainable-ml, machine-learning, markov-decision-processes
• Language: Python
• Homepage: https://arxiv.org/abs/2306.03929
• Size: 85.9 KB
• Stars: 4
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Finding Counterfactually Optimal Action Sequences in Continuous State Spaces

This repository contains the code used in the paper ["Finding Counterfactually Optimal Action Sequences in Continuous State Spaces"](https://arxiv.org/abs/2306.03929), published at NeurIPS 2023.

## Dependencies

All the experiments were performed using Python 3.9. In order to create a virtual environment and install the project dependencies you can run the following commands:

```bash

python3 -m venv env

source env/bin/activate

pip install -r requirements.txt

```

## Code organization

The directory [src](src/) contains the source code for the experiments.

The directory [scripts](scripts/) contains bash scripts that use the aforementioned code and pass parameter values required for the various experiments.

* ``build_datasets.sh`` is preprocessing the sepsis management data

* ``build_scm.sh`` is used to train the final SCM

* ``cv_scm.sh`` is used to evaluate the goodness of fit of the SCM via cross-validation, under different values of the networks' Lipschitz constants

* ``grande_experiment_slurm.sh`` is the main script used to run experiments using a slurm scheduler and this is where each experiment's configuration is set

* ``single_experiment_slurm.sh`` is a helper script called by ``grande_experiment_slurm.sh``

* ``solve_facility_location.sh`` is used to precompute anchor sets using the Facility-Location method

The directory [notebooks](notebooks/) contains jupyter notebooks producing the figures appearing in the paper. Each notebook reads output files generated by scripts. For details, see ``grande_experiment_slurm.sh`` and the description within each notebook.

The directory [figures](figures/) is used for saving the figures produced by the notebooks.

The directory [outputs](outputs/) and its sub-directories are used for saving the outputs generated by the scripts.

## Citation

If you use parts of the code in this repository for your own research, please consider citing:

    @inproceedings{tsirtsis2023finding,

        title={Finding Counterfactually Optimal Action Sequences in Continuous State Spaces},

        author={Tsirtsis, Stratis and Gomez-Rodriguez, Manuel},

        booktitle={Advances in Neural Information Processing Systems (NeurIPS)},

        year={2023}

    }