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Assumptions of IV Methods for Observational Epidemiology.
Open accessAbstract
Instrumental variable (IV) methods are becoming increasingly popular as they seem to offer the only viable way to overcome the problem of unobserved confounding in observational studies. However, some attention has to be paid to the details, as not all such methods target the same causal parameters and some rely on more restrictive parametric assumptions than others. We therefore discuss and contrast the most common IV approaches with relevance to typical applications in observational epidemiology. Further, we illustrate and compare the asymptotic bias of these IV estimators when underlying assumptions are violated in a numerical study. One of our conclusions is that all IV methods encounter problems in the presence of effect modification by unobserved confounders. Since this can never be ruled out for sure, we recommend that practical applications of IV estimators be accompanied routinely by a sensitivity analysis.
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“Assortative mating occurs when individuals reproduce with people who are more similar to themselves than would be expected by chance. This can also lead to genetic associations that represent social differences rather than causal effects 50 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [50]
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Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data
Burgess, S., Butterworth, A., & Thompson, S. G. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21758 · Cited by 6,050
Avoiding bias from weak instruments in Mendelian randomization studies
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Mendelian Randomization as an Approach to Assess Causality Using Observational Data
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Guidelines for performing Mendelian randomization investigations
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Metadata
- Authors
- Didelez, V., Meng, S., & Sheehan, N. A.
- Affiliations
- University of Leicester (GB)
- Corresponding author
- Didelez, Vanessa
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 2010-02-01
- Volume
- 25
- Issue
- 1
- Pages
- 22-40
- ISSN
- 0883-4237 (ISSN-L)
- 2168-8745
- OpenAlex
- W2949540592
- Cited by
- 214
- References
- 80
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 20 · 2023: 6 · 2022: 7 · 2021: 15 · 2020: 10 · 2019: 17 · 2018: 12 · 2017: 13 · 2016: 25 · 2015: 14 · 2014: 19 · 2013: 11 · 2012: 18
- FWCI
- 11.19
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Statistical Science (publishedVersion)
- Explore Bristol Research (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- Leicester Research Archive (University of Leicester) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Medical Research Council
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- instrumental variables, unmeasured confounding, causal inference, effect modification, asymptotic bias, sensitivity analysis, parametric assumptions, observational studies, causal parameters
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