Causal inference and observational data

Observational studies using causal inference frameworks can provide a feasible alternative to randomized controlled trials. Advances in statistics, machine learning, and access to big data facilitate unraveling complex causal relationships from observational data across healthcare, social sciences,...

Descripción completa

Detalles Bibliográficos
Autores principales: Olier, Ivan, Zhan, Yiqiang, Liang, Xiaoyu, Volovici, Victor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Editorial
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10566026/
https://www.ncbi.nlm.nih.gov/pubmed/37821812
http://dx.doi.org/10.1186/s12874-023-02058-5
_version_ 1785118829134741504
author Olier, Ivan
Zhan, Yiqiang
Liang, Xiaoyu
Volovici, Victor
author_facet Olier, Ivan
Zhan, Yiqiang
Liang, Xiaoyu
Volovici, Victor
author_sort Olier, Ivan
collection PubMed
description Observational studies using causal inference frameworks can provide a feasible alternative to randomized controlled trials. Advances in statistics, machine learning, and access to big data facilitate unraveling complex causal relationships from observational data across healthcare, social sciences, and other fields. However, challenges like evaluating models and bias amplification remain.
format Online
Article
Text
id pubmed-10566026
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-105660262023-10-12 Causal inference and observational data Olier, Ivan Zhan, Yiqiang Liang, Xiaoyu Volovici, Victor BMC Med Res Methodol Editorial Observational studies using causal inference frameworks can provide a feasible alternative to randomized controlled trials. Advances in statistics, machine learning, and access to big data facilitate unraveling complex causal relationships from observational data across healthcare, social sciences, and other fields. However, challenges like evaluating models and bias amplification remain. BioMed Central 2023-10-11 /pmc/articles/PMC10566026/ /pubmed/37821812 http://dx.doi.org/10.1186/s12874-023-02058-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Editorial
Olier, Ivan
Zhan, Yiqiang
Liang, Xiaoyu
Volovici, Victor
Causal inference and observational data
title Causal inference and observational data
title_full Causal inference and observational data
title_fullStr Causal inference and observational data
title_full_unstemmed Causal inference and observational data
title_short Causal inference and observational data
title_sort causal inference and observational data
topic Editorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10566026/
https://www.ncbi.nlm.nih.gov/pubmed/37821812
http://dx.doi.org/10.1186/s12874-023-02058-5
work_keys_str_mv AT olierivan causalinferenceandobservationaldata
AT zhanyiqiang causalinferenceandobservationaldata
AT liangxiaoyu causalinferenceandobservationaldata
AT volovicivictor causalinferenceandobservationaldata

Ejemplares similares