{"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/uncovering-causality-from-multivariate-hawkes","title":"Uncovering Causality from Multivariate Hawkes Integrated Cumulants","arxiv_id":"1607.06333","date":"2016-07-21","proceeding":"ICML 2017 8","authors":["Massil Achab","Emmanuel Bacry","Stéphane Gaïffas","Iacopo Mastromatteo","Jean-Francois Muzy"],"abstract":"We design a new nonparametric method that allows one to estimate the matrix\nof integrated kernels of a multivariate Hawkes process. This matrix not only\nencodes the mutual influences of each nodes of the process, but also\ndisentangles the causality relationships between them. Our approach is the\nfirst that leads to an estimation of this matrix without any parametric\nmodeling and estimation of the kernels themselves. A consequence is that it can\ngive an estimation of causality relationships between nodes (or users), based\non their activity timestamps (on a social network for instance), without\nknowing or estimating the shape of the activities lifetime. For that purpose,\nwe introduce a moment matching method that fits the third-order integrated\ncumulants of the process. We show on numerical experiments that our approach is\nindeed very robust to the shape of the kernels, and gives appealing results on\nthe MemeTracker database.","url_abs":"http://arxiv.org/abs/1607.06333v3","url_pdf":"http://arxiv.org/pdf/1607.06333v3.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":"uncovering-causality-from-multivariate-hawkes","repo_url":"https://github.com/achab/nphc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1607.06333","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}