{"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/merlin-mixture-effect-recovery-in-linear","title":"MERLiN: Mixture Effect Recovery in Linear Networks","arxiv_id":"1512.01255","date":"2015-12-03","proceeding":null,"authors":["Sebastian Weichwald","Moritz Grosse-Wentrup","Arthur Gretton"],"abstract":"Causal inference concerns the identification of cause-effect relationships\nbetween variables, e.g. establishing whether a stimulus affects activity in a\ncertain brain region. The observed variables themselves often do not constitute\nmeaningful causal variables, however, and linear combinations need to be\nconsidered. In electroencephalographic studies, for example, one is not\ninterested in establishing cause-effect relationships between electrode signals\n(the observed variables), but rather between cortical signals (the causal\nvariables) which can be recovered as linear combinations of electrode signals.\n  We introduce MERLiN (Mixture Effect Recovery in Linear Networks), a family of\ncausal inference algorithms that implement a novel means of constructing causal\nvariables from non-causal variables. We demonstrate through application to EEG\ndata how the basic MERLiN algorithm can be extended for application to\ndifferent (neuroimaging) data modalities. Given an observed linear mixture, the\nalgorithms can recover a causal variable that is a linear effect of another\ngiven variable. That is, MERLiN allows us to recover a cortical signal that is\naffected by activity in a certain brain region, while not being a direct effect\nof the stimulus. The Python/Matlab implementation for all presented algorithms\nis available on https://github.com/sweichwald/MERLiN","url_abs":"http://arxiv.org/abs/1512.01255v3","url_pdf":"http://arxiv.org/pdf/1512.01255v3.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":"merlin-mixture-effect-recovery-in-linear","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"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"}],"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}