{"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/kernel-based-conditional-independence-test","title":"Kernel-based Conditional Independence Test and Application in Causal Discovery","arxiv_id":"1202.3775","date":"2012-02-14","proceeding":null,"authors":["Kun Zhang","Jonas Peters","Dominik Janzing","Bernhard Schoelkopf"],"abstract":"Conditional independence testing is an important problem, especially in\nBayesian network learning and causal discovery. Due to the curse of\ndimensionality, testing for conditional independence of continuous variables is\nparticularly challenging. We propose a Kernel-based Conditional Independence\ntest (KCI-test), by constructing an appropriate test statistic and deriving its\nasymptotic distribution under the null hypothesis of conditional independence.\nThe proposed method is computationally efficient and easy to implement.\nExperimental results show that it outperforms other methods, especially when\nthe conditioning set is large or the sample size is not very large, in which\ncase other methods encounter difficulties.","url_abs":"http://arxiv.org/abs/1202.3775v1","url_pdf":"http://arxiv.org/pdf/1202.3775v1.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":"kernel-based-conditional-independence-test","repo_url":"https://github.com/yrodill/internship-Angers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"kernel-based-conditional-independence-test","repo_url":"https://github.com/christinaheinze/nonlinearICP-and-CondIndTests","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"causal-discovery","task_name":"Causal Discovery"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1202.3775","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}