{"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/mixed-graphical-models-for-causal-analysis-of","title":"Mixed Graphical Models for Causal Analysis of Multi-modal Variables","arxiv_id":"1704.02621","date":"2017-04-09","proceeding":null,"authors":["Andrew J Sedgewick","Joseph D. Ramsey","Peter Spirtes","Clark Glymour","Panayiotis V. Benos"],"abstract":"Graphical causal models are an important tool for knowledge discovery because\nthey can represent both the causal relations between variables and the\nmultivariate probability distributions over the data. Once learned, causal\ngraphs can be used for classification, feature selection and hypothesis\ngeneration, while revealing the underlying causal network structure and thus\nallowing for arbitrary likelihood queries over the data. However, current\nalgorithms for learning sparse directed graphs are generally designed to handle\nonly one type of data (continuous-only or discrete-only), which limits their\napplicability to a large class of multi-modal biological datasets that include\nmixed type variables. To address this issue, we developed new methods that\nmodify and combine existing methods for finding undirected graphs with methods\nfor finding directed graphs. These hybrid methods are not only faster, but also\nperform better than the directed graph estimation methods alone for a variety\nof parameter settings and data set sizes. Here, we describe a new conditional\nindependence test for learning directed graphs over mixed data types and we\ncompare performances of different graph learning strategies on synthetic data.","url_abs":"http://arxiv.org/abs/1704.02621v1","url_pdf":"http://arxiv.org/pdf/1704.02621v1.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":"mixed-graphical-models-for-causal-analysis-of","repo_url":"https://github.com/benoslab/causalmgm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"graph-learning","task_name":"Graph Learning"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}