Olier, I, Zhan, Y, Liang, X and Volovici, V (2023) Causal inference and observational data. BMC Medical Research Methodology, 23 (1).

Preview

Text s12874-023-02058-5.pdf - Published Version Available under License Creative Commons Attribution. Download (547kB) | Preview

Abstract

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.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	1117 Public Health and Health Services; General & Internal Medicine
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science & Mathematics
Publisher:	Springer Science and Business Media LLC
SWORD Depositor:	A Symplectic
Date Deposited:	12 Oct 2023 12:31
Last Modified:	12 Oct 2023 12:31
DOI or ID number:	10.1186/s12874-023-02058-5
URI:	https://researchonline.ljmu.ac.uk/id/eprint/21711

View Item