Cited by 214Advanced Causal Inference TechniquesStatistical Methods in EpidemiologyMeta-analysis and systematic reviews

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Assumptions of IV Methods for Observational Epidemiology.

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Didelez, Vanessa, Meng, Sha, Sheehan, Nuala A.·2010·Statistical Science

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Abstract

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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Authors: Didelez, V., Meng, S., & Sheehan, N. A.
Affiliations: University of Leicester (GB)
Corresponding author: Didelez, Vanessa
ORCID: Didelez, Vanessa
Sheehan, Nuala A.
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
DOI: 10.1214/09-sts316
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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