{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gcastle-a-python-toolbox-for-causal-discovery","title":"gCastle: A Python Toolbox for Causal Discovery","arxiv_id":"2111.15155","date":"2021-11-30","proceeding":null,"authors":["Keli Zhang","Shengyu Zhu","Marcus Kalander","Ignavier Ng","Junjian Ye","Zhitang Chen","Lujia Pan"],"abstract":"$\\texttt{gCastle}$ is an end-to-end Python toolbox for causal structure learning. It provides functionalities of generating data from either simulator or real-world dataset, learning causal structure from the data, and evaluating the learned graph, together with useful practices such as prior knowledge insertion, preliminary neighborhood selection, and post-processing to remove false discoveries. Compared with related packages, $\\texttt{gCastle}$ includes many recently developed gradient-based causal discovery methods with optional GPU acceleration. $\\texttt{gCastle}$ brings convenience to researchers who may directly experiment with the code as well as practitioners with graphical user interference. Three real-world datasets in telecommunications are also provided in the current version. $\\texttt{gCastle}$ is available under Apache License 2.0 at \\url{https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle}.","url_abs":"https://arxiv.org/abs/2111.15155v1","url_pdf":"https://arxiv.org/pdf/2111.15155v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gcastle-a-python-toolbox-for-causal-discovery","repo_url":"https://github.com/huawei-noah/trustworthyAI","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"gcastle-a-python-toolbox-for-causal-discovery","repo_url":"https://github.com/ErdunGAO/FedDAG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"causal-discovery","task_name":"Causal Discovery"},{"task_slug":null,"task_name":"GPU"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2111.15155","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}