{"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-with-multiple-treatments-and","title":"Uplift Modeling with Multiple Treatments and General Response Types","arxiv_id":"1705.08492","date":"2017-05-23","proceeding":null,"authors":["Yan Zhao","Xiao Fang","David Simchi-Levi"],"abstract":"Randomized experiments have been used to assist decision-making in many\nareas. They help people select the optimal treatment for the test population\nwith certain statistical guarantee. However, subjects can show significant\nheterogeneity in response to treatments. The problem of customizing treatment\nassignment based on subject characteristics is known as uplift modeling,\ndifferential response analysis, or personalized treatment learning in\nliterature. A key feature for uplift modeling is that the data is unlabeled. It\nis impossible to know whether the chosen treatment is optimal for an individual\nsubject because response under alternative treatments is unobserved. This\npresents a challenge to both the training and the evaluation of uplift models.\nIn this paper we describe how to obtain an unbiased estimate of the key\nperformance metric of an uplift model, the expected response. We present a new\nuplift algorithm which creates a forest of randomized trees. The trees are\nbuilt with a splitting criterion designed to directly optimize their uplift\nperformance based on the proposed evaluation method. Both the evaluation method\nand the algorithm apply to arbitrary number of treatments and general response\ntypes. Experimental results on synthetic data and industry-provided data show\nthat our algorithm leads to significant performance improvement over other\napplicable methods.","url_abs":"http://arxiv.org/abs/1705.08492v1","url_pdf":"http://arxiv.org/pdf/1705.08492v1.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-with-multiple-treatments-and","repo_url":"https://github.com/Ibotta/mr_uplift","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"uplift-modeling-with-multiple-treatments-and","repo_url":"https://github.com/Matthias2193/APA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1705.08492","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1705.08492"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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