{"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/recovery-of-non-linear-cause-effect","title":"Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data","arxiv_id":"1605.00391","date":"2016-05-02","proceeding":null,"authors":["Sebastian Weichwald","Arthur Gretton","Bernhard Schölkopf","Moritz Grosse-Wentrup"],"abstract":"Causal inference concerns the identification of cause-effect relationships\nbetween variables. However, often only linear combinations of variables\nconstitute meaningful causal variables. For example, recovering the signal of a\ncortical source from electroencephalography requires a well-tuned combination\nof signals recorded at multiple electrodes. We recently introduced the MERLiN\n(Mixture Effect Recovery in Linear Networks) algorithm that is able to recover,\nfrom an observed linear mixture, a causal variable that is a linear effect of\nanother given variable. Here we relax the assumption of this cause-effect\nrelationship being linear and present an extended algorithm that can pick up\nnon-linear cause-effect relationships. Thus, the main contribution is an\nalgorithm (and ready to use code) that has broader applicability and allows for\na richer model class. Furthermore, a comparative analysis indicates that the\nassumption of linear cause-effect relationships is not restrictive in analysing\nelectroencephalographic data.","url_abs":"http://arxiv.org/abs/1605.00391v2","url_pdf":"http://arxiv.org/pdf/1605.00391v2.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":"recovery-of-non-linear-cause-effect","repo_url":"https://github.com/sweichwald/MERLiN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"causal-inference","task_name":"Causal Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}