{"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/interval-estimation-of-individual-level","title":"Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding","arxiv_id":"1810.02894","date":"2018-10-05","proceeding":null,"authors":["Nathan Kallus","Xiaojie Mao","Angela Zhou"],"abstract":"We study the problem of learning conditional average treatment effects (CATE)\nfrom observational data with unobserved confounders. The CATE function maps\nbaseline covariates to individual causal effect predictions and is key for\npersonalized assessments. Recent work has focused on how to learn CATE under\nunconfoundedness, i.e., when there are no unobserved confounders. Since CATE\nmay not be identified when unconfoundedness is violated, we develop a\nfunctional interval estimator that predicts bounds on the individual causal\neffects under realistic violations of unconfoundedness. Our estimator takes the\nform of a weighted kernel estimator with weights that vary adversarially. We\nprove that our estimator is sharp in that it converges exactly to the tightest\nbounds possible on CATE when there may be unobserved confounders. Further, we\nstudy personalized decision rules derived from our estimator and prove that\nthey achieve optimal minimax regret asymptotically. We assess our approach in a\nsimulation study as well as demonstrate its application in the case of hormone\nreplacement therapy by comparing conclusions from a real observational study\nand clinical trial.","url_abs":"http://arxiv.org/abs/1810.02894v1","url_pdf":"http://arxiv.org/pdf/1810.02894v1.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":"interval-estimation-of-individual-level","repo_url":"https://github.com/valentyn1997/au-cnfs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1810.02894","atlas_url":"https://app.syntology.ai/?focus=1810.02894","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}