{"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/debiasing-the-estimate-of-treatment-effect-on","title":"Debiasing the estimate of treatment effect on the treated with time-varying counfounders","arxiv_id":"2204.03447","date":"2022-04-07","proceeding":null,"authors":["Camille Nevoret","Sandrine Katsahian","Agathe Guilloux"],"abstract":"With the increased availability of large health databases comes the opportunity of evaluating treatment effect on new data sources.Through these databases time-dependent outcomes can be analysed as events that can be measured using counting processes. Estimating average treatment effect on the treated (ATT) requires modelling of time-varying covariate and time-dependent treatment and outcome. Gran et al. proposed an easy-to-implement method based on additive intensity regression to estimate ATT. We introduce a debiased estimate of the ATT based on a generalization of the Gran's model for a potentially repeated outcome and in the presence of multiple time-dependent covariates and baseline covariates. Simulation analyses show that our corrected estimator outperforms Gran's uncorrected estimator. Our method is applied to intensive care real-life data from MIMIC-III databases to estimate vasoppressors effect on patients with sepsis.","url_abs":"https://arxiv.org/abs/2204.03447v1","url_pdf":"https://arxiv.org/pdf/2204.03447v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"debiasing-the-estimate-of-treatment-effect-on","repo_url":"https://gitlab.com/camille.nevoret/att-estimation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}