{"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/causal-simulations-for-uplift-modeling","title":"Causal Simulations for Uplift Modeling","arxiv_id":"1902.00287","date":"2019-02-01","proceeding":null,"authors":["Jeroen Berrevoets","Wouter Verbeke"],"abstract":"Uplift modeling requires experimental data, preferably collected in random\nfashion. This places a logistical and financial burden upon any organisation\naspiring such models. Once deployed, uplift models are subject to effects from\nconcept drift. Hence, methods are being developed that are able to learn from\nnewly gained experience, as well as handle drifting environments. As these new\nmethods attempt to eliminate the need for experimental data, another approach\nto test such methods must be formulated. Therefore, we propose a method to\nsimulate environments that offer causal relationships in their parameters.","url_abs":"http://arxiv.org/abs/1902.00287v1","url_pdf":"http://arxiv.org/pdf/1902.00287v1.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":"causal-simulations-for-uplift-modeling","repo_url":"https://github.com/vub-dl/cs-um","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}