{"id":20723631,"url":"https://github.com/networks-learning/counterfactual-continuous-mdp","last_synced_at":"2025-04-23T17:27:32.538Z","repository":{"id":200275432,"uuid":"647912703","full_name":"Networks-Learning/counterfactual-continuous-mdp","owner":"Networks-Learning","description":"Code for \"Finding Counterfactually Optimal Action Sequences in Continuous State Spaces\", NeurIPS 2023.","archived":false,"fork":false,"pushed_at":"2023-10-18T06:59:45.000Z","size":88,"stargazers_count":6,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-30T01:51:45.694Z","etag":null,"topics":["causality","counterfactuals","decision-making","decision-making-under-uncertainty","explainable-ai","explainable-ml","machine-learning","markov-decision-processes"],"latest_commit_sha":null,"homepage":"https://arxiv.org/abs/2306.03929","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Networks-Learning.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2023-05-31T19:48:56.000Z","updated_at":"2024-10-16T08:50:06.000Z","dependencies_parsed_at":"2023-10-16T11:11:50.559Z","dependency_job_id":null,"html_url":"https://github.com/Networks-Learning/counterfactual-continuous-mdp","commit_stats":null,"previous_names":["networks-learning/counterfactual-continuous-mdp"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Networks-Learning%2Fcounterfactual-continuous-mdp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Networks-Learning%2Fcounterfactual-continuous-mdp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Networks-Learning%2Fcounterfactual-continuous-mdp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Networks-Learning%2Fcounterfactual-continuous-mdp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Networks-Learning","download_url":"https://codeload.github.com/Networks-Learning/counterfactual-continuous-mdp/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250479615,"owners_count":21437395,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["causality","counterfactuals","decision-making","decision-making-under-uncertainty","explainable-ai","explainable-ml","machine-learning","markov-decision-processes"],"created_at":"2024-11-17T04:09:16.458Z","updated_at":"2025-04-23T17:27:32.516Z","avatar_url":"https://github.com/Networks-Learning.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Finding Counterfactually Optimal Action Sequences in Continuous State Spaces\n\nThis 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.\n\n## Dependencies\n\nAll 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:\n\n```bash\npython3 -m venv env\nsource env/bin/activate\npip install -r requirements.txt\n```\n\n## Code organization\n\nThe directory [src](src/) contains the source code for the experiments.\n\nThe directory [scripts](scripts/) contains bash scripts that use the aforementioned code and pass parameter values required for the various experiments.\n\n* ``build_datasets.sh`` is preprocessing the sepsis management data\n* ``build_scm.sh`` is used to train the final SCM\n* ``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\n* ``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\n* ``single_experiment_slurm.sh`` is a helper script called by ``grande_experiment_slurm.sh``\n* ``solve_facility_location.sh`` is used to precompute anchor sets using the Facility-Location method\n\nThe 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.\n\nThe directory [figures](figures/) is used for saving the figures produced by the notebooks.\n\nThe directory [outputs](outputs/) and its sub-directories are used for saving the outputs generated by the scripts.\n\n## Citation\n\nIf you use parts of the code in this repository for your own research, please consider citing:\n\n    @inproceedings{tsirtsis2023finding,\n        title={Finding Counterfactually Optimal Action Sequences in Continuous State Spaces},\n        author={Tsirtsis, Stratis and Gomez-Rodriguez, Manuel},\n        booktitle={Advances in Neural Information Processing Systems (NeurIPS)},\n        year={2023}\n    }\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnetworks-learning%2Fcounterfactual-continuous-mdp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnetworks-learning%2Fcounterfactual-continuous-mdp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnetworks-learning%2Fcounterfactual-continuous-mdp/lists"}