{"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/uplift-modeling-from-separate-labels","title":"Uplift Modeling from Separate Labels","arxiv_id":"1803.05112","date":"2018-03-14","proceeding":"NeurIPS 2018 12","authors":["Ikko Yamane","Florian Yger","Jamal Atif","Masashi Sugiyama"],"abstract":"Uplift modeling is aimed at estimating the incremental impact of an action on\nan individual's behavior, which is useful in various application domains such\nas targeted marketing (advertisement campaigns) and personalized medicine\n(medical treatments). Conventional methods of uplift modeling require every\ninstance to be jointly equipped with two types of labels: the taken action and\nits outcome. However, obtaining two labels for each instance at the same time\nis difficult or expensive in many real-world problems. In this paper, we\npropose a novel method of uplift modeling that is applicable to a more\npractical setting where only one type of labels is available for each instance.\nWe show a mean squared error bound for the proposed estimator and demonstrate\nits effectiveness through experiments.","url_abs":"http://arxiv.org/abs/1803.05112v5","url_pdf":"http://arxiv.org/pdf/1803.05112v5.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":"uplift-modeling-from-separate-labels","repo_url":"https://github.com/i-yamane/uplift","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"marketing","task_name":"Marketing"}],"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}