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On model selection and model misspecification in causal inference.
Open accessAbstract
Standard variable selection procedures, primarily developed for the construction of outcome prediction models, are routinely applied when assessing exposure effects in observational studies. We argue that this tradition is sub-optimal and prone to yield bias in exposure effect estimators as well as their corresponding uncertainty estimators. We weigh the pros and cons of confounder-selection procedures and propose a procedure directly targeting the quality of the exposure effect estimator. We further demonstrate that certain strategies for inferring causal effects have the desirable features (a) of producing (approximately) valid confidence intervals, even when the confounder-selection process is ignored, and (b) of being robust against certain forms of misspecification of the association of confounders with both exposure and outcome.
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“These potential sources of bias have encouraged the development of statistical approaches and datasets to perform within-family Mendelian randomization analyses, exploiting the random allocation of variants between siblings 51 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [51]
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Metadata
- Authors
- Vansteelandt, S., Bekaert, M., & Claeskens, G.
- Affiliations
- Ghent University (BE)
- KU Leuven (BE)
- Corresponding author
- Vansteelandt, Stijn
- Venue
- Statistical Methods in Medical Research
- Publisher
- SAGE Publications
- Published
- 2010-11-12
- Volume
- 21
- Issue
- 1
- Pages
- 7-30
- ISSN
- 0962-2802 (ISSN-L)
- 1477-0334
- OpenAlex
- W2107263285
- Cited by
- 172
- References
- 70
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 18 · 2023: 11 · 2022: 13 · 2021: 14 · 2020: 15 · 2019: 10 · 2018: 11 · 2017: 15 · 2016: 9 · 2015: 11 · 2014: 13 · 2013: 6 · 2012: 10
- FWCI
- 5.41
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Statistical Methods in Medical Research (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- PubMed (publishedVersion)
- Lirias (submittedVersion, other-oa)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- Lirias (KU Leuven) (submittedVersion)
- Funders
- Vlaamse regering
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- model selection, model misspecification, causal inference, confounder selection, exposure effect estimation, confidence intervals, observational studies
- MeSH terms
- Computer Simulation, Data Interpretation, Statistical, Cardiac Catheterization, Humans, Models, Statistical, Bias, Causality, Confounding Factors, Epidemiologic, Biomedical Research
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