Scalable confounding adjustment in real-world evidence: benchmarking data-adaptive and investigator-specified strategies in a large-scale trial emulation study
Data-adaptive algorithms show promise for scalable confounding adjustment in large-scale evidence systems and as augmentation tools for investigator-specified designs. Hybrid strategies combining algorithmic methods with investigator expertise offer the most reliable approach for individual causal questions.
Authors
- Sebastian Schneeweiß (ORCID: https://orcid.org/0000-0003-2575-467X)
- Richard Wyss
- Shirley Wang (ORCID: https://orcid.org/0000-0001-7761-7090)
- Andrew R. Weckstein (ORCID: https://orcid.org/0000-0002-6227-6796)
Institutions
- Brigham and Women's Hospital (US)
- Harvard University (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-11-12
- DOI
- https://doi.org/10.1093/jamia/ocaf204
- Primary Topic
- Advanced Causal Inference Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00