{"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/predictive-independence-testing-predictive","title":"Predictive Independence Testing, Predictive Conditional Independence Testing, and Predictive Graphical Modelling","arxiv_id":"1711.05869","date":"2017-11-16","proceeding":null,"authors":["Samuel Burkart","Franz J. Király"],"abstract":"Testing (conditional) independence of multivariate random variables is a task\ncentral to statistical inference and modelling in general - though\nunfortunately one for which to date there does not exist a practicable\nworkflow. State-of-art workflows suffer from the need for heuristic or\nsubjective manual choices, high computational complexity, or strong parametric\nassumptions.\n  We address these problems by establishing a theoretical link between\nmultivariate/conditional independence testing, and model comparison in the\nmultivariate predictive modelling aka supervised learning task. This link\nallows advances in the extensively studied supervised learning workflow to be\ndirectly transferred to independence testing workflows - including automated\ntuning of machine learning type which addresses the need for a heuristic\nchoice, the ability to quantitatively trade-off computational demand with\naccuracy, and the modern black-box philosophy for checking and interfacing.\n  As a practical implementation of this link between the two workflows, we\npresent a python package 'pcit', which implements our novel multivariate and\nconditional independence tests, interfacing the supervised learning API of the\nscikit-learn package. Theory and package also allow for straightforward\nindependence test based learning of graphical model structure.\n  We empirically show that our proposed predictive independence test outperform\nor are on par to current practice, and the derived graphical model structure\nlearning algorithms asymptotically recover the 'true' graph. This paper, and\nthe 'pcit' package accompanying it, thus provide powerful, scalable,\ngeneralizable, and easy-to-use methods for multivariate and conditional\nindependence testing, as well as for graphical model structure learning.","url_abs":"http://arxiv.org/abs/1711.05869v2","url_pdf":"http://arxiv.org/pdf/1711.05869v2.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":"predictive-independence-testing-predictive","repo_url":"https://github.com/alan-turing-institute/pcit","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"philosophy","task_name":"Philosophy"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}