{"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/causal-inference-with-noisy-and-missing","title":"Causal Inference with Noisy and Missing Covariates via Matrix Factorization","arxiv_id":"1806.00811","date":"2018-06-03","proceeding":"NeurIPS 2018 12","authors":["Nathan Kallus","Xiaojie Mao","Madeleine Udell"],"abstract":"Valid causal inference in observational studies often requires controlling\nfor confounders. However, in practice measurements of confounders may be noisy,\nand can lead to biased estimates of causal effects. We show that we can reduce\nthe bias caused by measurement noise using a large number of noisy measurements\nof the underlying confounders. We propose the use of matrix factorization to\ninfer the confounders from noisy covariates, a flexible and principled\nframework that adapts to missing values, accommodates a wide variety of data\ntypes, and can augment many causal inference methods. We bound the error for\nthe induced average treatment effect estimator and show it is consistent in a\nlinear regression setting, using Exponential Family Matrix Completion\npreprocessing. We demonstrate the effectiveness of the proposed procedure in\nnumerical experiments with both synthetic data and real clinical data.","url_abs":"http://arxiv.org/abs/1806.00811v1","url_pdf":"http://arxiv.org/pdf/1806.00811v1.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":"causal-inference-with-noisy-and-missing","repo_url":"https://github.com/udellgroup/causal_mf_code","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"causal-inference","task_name":"Causal Inference"},{"task_slug":"matrix-completion","task_name":"Matrix Completion"},{"task_slug":"missing-values","task_name":"Missing Values"},{"task_slug":null,"task_name":"valid"}],"methods":[{"method_slug":"causal-inference","method_name":"Causal inference"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.00811","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}