{"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/fast-estimation-of-causal-interactions-using","title":"Fast Estimation of Causal Interactions using Wold Processes","arxiv_id":"1807.04595","date":"2018-07-12","proceeding":"NeurIPS 2018 12","authors":["Flavio Figueiredo","Guilherme Borges","Pedro O. S. Vaz de Melo","Renato M. Assunção"],"abstract":"We here focus on the task of learning Granger causality matrices for\nmultivariate point processes. In order to accomplish this task, our work is the\nfirst to explore the use of Wold processes. By doing so, we are able to develop\nasymptotically fast MCMC learning algorithms. With $N$ being the total number\nof events and $K$ the number of processes, our learning algorithm has a\n$O(N(\\,\\log(N)\\,+\\,\\log(K)))$ cost per iteration. This is much faster than the\n$O(N^3\\,K^2)$ or $O(K^3)$ for the state of the art. Our approach, called\nGrangerBusca, is validated on nine datasets. This is an advance in relation to\nmost prior efforts which focus mostly on subsets of the Memetracker data.\nRegarding accuracy, GrangerBusca is three times more accurate (in Precision@10)\nthan the state of the art for the commonly explored subsets Memetracker. Due to\nGrangerBusca's much lower training complexity, our approach is the only one\nable to train models for larger, full, sets of data.","url_abs":"http://arxiv.org/abs/1807.04595v2","url_pdf":"http://arxiv.org/pdf/1807.04595v2.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":"fast-estimation-of-causal-interactions-using","repo_url":"https://github.com/flaviovdf/granger-busca","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"point-processes","task_name":"Point Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}