{"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/recursive-partitioning-for-personalization","title":"Recursive Partitioning for Personalization using Observational Data","arxiv_id":"1608.08925","date":"2016-08-31","proceeding":"ICML 2017 8","authors":["Nathan Kallus"],"abstract":"We study the problem of learning to choose from m discrete treatment options\n(e.g., news item or medical drug) the one with best causal effect for a\nparticular instance (e.g., user or patient) where the training data consists of\npassive observations of covariates, treatment, and the outcome of the\ntreatment. The standard approach to this problem is regress and compare: split\nthe training data by treatment, fit a regression model in each split, and, for\na new instance, predict all m outcomes and pick the best. By reformulating the\nproblem as a single learning task rather than m separate ones, we propose a new\napproach based on recursively partitioning the data into regimes where\ndifferent treatments are optimal. We extend this approach to an optimal\npartitioning approach that finds a globally optimal partition, achieving a\ncompact, interpretable, and impactful personalization model. We develop new\ntools for validating and evaluating personalization models on observational\ndata and use these to demonstrate the power of our novel approaches in a\npersonalized medicine and a job training application.","url_abs":"http://arxiv.org/abs/1608.08925v3","url_pdf":"http://arxiv.org/pdf/1608.08925v3.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":"recursive-partitioning-for-personalization","repo_url":"https://github.com/Aida-Rahmattalabi/PersonalizationTrees","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1608.08925","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}