Reading and conducting instrumental variable studies: guide, glossary, and checklist
Instrumental variable analysis uses naturally occurring variation to estimate the causal effects of treatments, interventions, and risk factors on outcomes in the population from observational data. Under specific assumptions, instrumental variable methods can provide unbiased estimates of causal effects. This article explains these assumptions and the information and tests typically reported in instrumental variable studies, which can assess the credibility of the findings of instrumental variable studies.
Authors
- Timothy Feeney
- Neil M Davies (ORCID: https://orcid.org/0000-0002-2460-0508)
- Eleanor Sanderson (ORCID: https://orcid.org/0000-0001-5188-5775)
- Venexia M. Walker (ORCID: https://orcid.org/0000-0001-5064-446X)
- Michael G. Levin (ORCID: https://orcid.org/0000-0002-9937-9932)
- Scott M Damraurer
Institutions
- University of North Carolina at Chapel Hill (US)
- Norwegian University of Science and Technology (NO)
- University of Bristol (GB)
- Philadelphia VA Medical Center (US)
- University College London (GB)
- University of Pennsylvania (US)
- Medical Research Council (GB)
Publication Details
- Journal
- BMJ
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1136/bmj-2023-078093
- Citations
- 18
- Primary Topic
- Advanced Causal Inference Techniques
- Type
- article
- Field-Weighted Citation Impact
- 12.76
Funders
- Medical Research Council