{"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-heterogeneous","title":"Recursive Partitioning for Heterogeneous Causal Effects","arxiv_id":"1504.01132","date":"2015-04-05","proceeding":null,"authors":["Susan Athey","Guido Imbens"],"abstract":"In this paper we study the problems of estimating heterogeneity in causal\neffects in experimental or observational studies and conducting inference about\nthe magnitude of the differences in treatment effects across subsets of the\npopulation. In applications, our method provides a data-driven approach to\ndetermine which subpopulations have large or small treatment effects and to\ntest hypotheses about the differences in these effects. For experiments, our\nmethod allows researchers to identify heterogeneity in treatment effects that\nwas not specified in a pre-analysis plan, without concern about invalidating\ninference due to multiple testing. In most of the literature on supervised\nmachine learning (e.g. regression trees, random forests, LASSO, etc.), the goal\nis to build a model of the relationship between a unit's attributes and an\nobserved outcome. A prominent role in these methods is played by\ncross-validation which compares predictions to actual outcomes in test samples,\nin order to select the level of complexity of the model that provides the best\npredictive power. Our method is closely related, but it differs in that it is\ntailored for predicting causal effects of a treatment rather than a unit's\noutcome. The challenge is that the \"ground truth\" for a causal effect is not\nobserved for any individual unit: we observe the unit with the treatment, or\nwithout the treatment, but not both at the same time. Thus, it is not obvious\nhow to use cross-validation to determine whether a causal effect has been\naccurately predicted. We propose several novel cross-validation criteria for\nthis problem and demonstrate through simulations the conditions under which\nthey perform better than standard methods for the problem of causal effects. We\nthen apply the method to a large-scale field experiment re-ranking results on a\nsearch engine.","url_abs":"http://arxiv.org/abs/1504.01132v3","url_pdf":"http://arxiv.org/pdf/1504.01132v3.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-heterogeneous","repo_url":"https://github.com/susanathey/causalTree","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"re-ranking","task_name":"Re-Ranking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1504.01132","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}