Causal inference and observational data
- PMID: 37821812
- PMCID: PMC10566026
- DOI: 10.1186/s12874-023-02058-5
Causal inference and observational data
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.
© 2023. BioMed Central Ltd., part of Springer Nature.
Conflict of interest statement
The authors of this editorial are Editorial Board Members of BMC Medical Research Methodology and Guest Editors of the Causal Inference and Observational Data collection.
References
-
- Hernán MA, Methods of Public Health Research — Strengthening Causal Inference from Observational Data. New England Journal of Medicine [Internet]. 2021 Oct 7 [cited 2023 May 23];385(15):1345–8. Available from: https://www.nejm.org/doi/full/10.1056/NEJMp2113319. - PubMed
-
- Rohlfing I, Zuber CI. Check Your Truth Conditions!Clarifying the Relationship between Theories of Causation and Social Science Methods for Causal Inference. Sociol Methods Res [Internet]. 2021 Nov 1 [cited 2023 May 23];50(4):1623–59. Available from: https://journals.sagepub.com/doi/10.1177/0049124119826156.
-
- Varian HR, Proceedings of the National Academy of Sciences [Internet]. Causal inference in economics and marketing. 2016 Jul 5 [cited 2023 May 23];113(27):7310–5. Available from: https://www.pnas.org/doi/abs/10.1073/pnas.1510479113. - PMC - PubMed
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources