{"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/backshift-learning-causal-cyclic-graphs-from","title":"backShift: Learning causal cyclic graphs from unknown shift interventions","arxiv_id":"1506.02494","date":"2015-06-08","proceeding":"NeurIPS 2015 12","authors":["Dominik Rothenhäusler","Christina Heinze","Jonas Peters","Nicolai Meinshausen"],"abstract":"We propose a simple method to learn linear causal cyclic models in the\npresence of latent variables. The method relies on equilibrium data of the\nmodel recorded under a specific kind of interventions (\"shift interventions\").\nThe location and strength of these interventions do not have to be known and\ncan be estimated from the data. Our method, called backShift, only uses second\nmoments of the data and performs simple joint matrix diagonalization, applied\nto differences between covariance matrices. We give a sufficient and necessary\ncondition for identifiability of the system, which is fulfilled almost surely\nunder some quite general assumptions if and only if there are at least three\ndistinct experimental settings, one of which can be pure observational data. We\ndemonstrate the performance on some simulated data and applications in flow\ncytometry and financial time series. The code is made available as R-package\nbackShift.","url_abs":"http://arxiv.org/abs/1506.02494v3","url_pdf":"http://arxiv.org/pdf/1506.02494v3.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":"backshift-learning-causal-cyclic-graphs-from","repo_url":"https://github.com/christinaheinze/backShift","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1506.02494","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}