{"id":27016440,"url":"https://github.com/kougioulis/cs-673-project","last_synced_at":"2025-09-02T04:40:34.308Z","repository":{"id":245011440,"uuid":"803766182","full_name":"kougioulis/CS-673-project","owner":"kougioulis","description":"Deep End-to-end Causal Inference for Time-series - Project for CS-673 (Intro to Deep Generative Models)","archived":false,"fork":false,"pushed_at":"2024-06-17T11:53:16.000Z","size":141158,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-09-01T20:57:47.385Z","etag":null,"topics":["causal-discovery","causality","generative-models"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kougioulis.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-05-21T10:51:42.000Z","updated_at":"2025-08-03T15:39:53.000Z","dependencies_parsed_at":null,"dependency_job_id":"55af1625-eab8-41cc-931e-3c19956261f2","html_url":"https://github.com/kougioulis/CS-673-project","commit_stats":null,"previous_names":["kougioulis/cs-673","kougioulis/cs-673-project"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/kougioulis/CS-673-project","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kougioulis%2FCS-673-project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kougioulis%2FCS-673-project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kougioulis%2FCS-673-project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kougioulis%2FCS-673-project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kougioulis","download_url":"https://codeload.github.com/kougioulis/CS-673-project/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kougioulis%2FCS-673-project/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273233240,"owners_count":25068725,"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","status":"online","status_checked_at":"2025-09-02T02:00:09.530Z","response_time":77,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["causal-discovery","causality","generative-models"],"created_at":"2025-04-04T15:20:06.000Z","updated_at":"2025-09-02T04:40:34.264Z","avatar_url":"https://github.com/kougioulis.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🎓 CS-673 Project (Intro to Deep Generative Models)\n\n---\n\n## 📜 Overview\n\nThe task of **Causal Discovery** is to uncover the true DAG $\\mathcal{G}$ given a dataset $D$ such that $p\\_D ∼ \\mathcal{G}$. Since the work of [ZARX18], which transformed the task of causal discovery to a continuous optimization program with acyclicity constraints, significant attention has shifted to deep learning-based causal discovery algorithms.\n\nIn this project, we select **DECI (Deep End-to-end Causal Inference)**, a SOTA causal discovery algorithm for iid observational data by [GAF+22], a variational inference model modeling exogenous noise as a normalizing flow.\n\nSince the literature on causal discovery algorithms for time-series data using deep learning techniques is quite limited and virginal, we opt to implement DECI on time-series data using lagged cross-sectional data. Finally, we evaluate our performance against non-deep-learning-inspired algorithms (PCMCI, PCMCI(+) etc.) on time-series synthetic data with known ground truth causal graph [LKSS20].\n\n---\n\n![DECI](assets/DECI.png)\n\n---\n\n## 📚 References\n\n- [**GAF+22**] Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, et al. *Deep end-to-end causal inference*. arXiv preprint arXiv:2202.02195, 2022.\n- [**LKSS20**] Andrew R. Lawrence, Marcus Kaiser, Rui Sampaio, and Maksim Sipos. *Data generating process to evaluate causal discovery techniques for time series data*. Causal Discovery Causality-Inspired Machine Learning Workshop at Neural Information Processing Systems, 2020.\n- [**VCB22**] Matthew J Vowels, Necati Cihan Camgoz, and Richard Bowden. *D’ya like DAGs? a survey on structure learning and causal discovery*. ACM Computing Surveys, 55(4):1–36, 2022.\n- [**ZARX18**] Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing. *Dags with NO-TEARS: Continuous optimization for structure learning*. Advances in Neural Information Processing Systems, 31, 2018.\n\n---\n\n## 🛠️ Setup Instructions\n\nThere are two separate environments that need to be configured to reproduce this project: **CDML** and **DECI (causica)**.\n\n### 🐍 Creating Virtual Environments\n\nYou may create the virtual environments with their respective requirements using the provided `.yml` files, using for example your Anaconda installation, on your shell as\n\n1. For CDML:\n   ```sh\n    conda env create -f environment-cdml.yml\n   ```\n\n2. For causica:\n   ```sh\n   conda env create -f environment-causica.yml\n   ```\n\n### 📌 Environment Details\n\n- The first environment runs on Python 3.8.19.\n- The second environment runs on Python 3.10.1, 🔥 PyTorch 1.13.0 and PyTorch lightning 2.2.2. \n\n### 🥰 Reproducing the experiments\n\n- Run `generate_dataset.ipynb` to generate a CDML configuration, plot the causal graph and generate the corresponding time-lagged dataset.\n\n- Run `experiments.ipynb` to run a DECI model on a CDML configuration, compute the metrics and compare to the ground truth graph, as well as PCMCI.\n\n- Run `RunAll.ipynb` to evaluate all pre-trained DECI models on each datasetavailable at the `datasets` folder and compare with PCMCI.\n\n---\n\nEnjoy! 🚀\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkougioulis%2Fcs-673-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkougioulis%2Fcs-673-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkougioulis%2Fcs-673-project/lists"}