The DOI matches its Crossref record, with a small difference in title or year; 68 of the 70 references it cites were found in a registry or an index.
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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Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Marginal Structural Models and Causal Inference in Epidemiology
Robins, J. M., Hernán, M. Á., & Brumback, B. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00011 · Cited by 5,581
Causal Diagrams for Epidemiologic Research
Greenland, S., Pearl, J., & Robins, J. M. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00008 · Cited by 3,728
Estimating Causal Effects from Large Data Sets Using Propensity Scores
Rubin, D. V. (1997) · Annals of Internal Medicine · doi:10.7326/0003-4819-127-8_part_2-199710151-00064 · Cited by 3,104
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Schwartz, J. D. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00005 · Cited by 3,045
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Maldonado, G., & Greenland, S. (1993) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a116813 · Cited by 2,491
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Guidelines for performing Mendelian randomization investigations
Burgess, S., Smith, G. D., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Holmes, M. V., Minelli, C., Relton, C. L., et al. (2019) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15555.1 · Cited by 1,828
Variable selection – A review and recommendations for the practicing statistician
Heinze, G., Wallisch, C., & Dunkler, D. (2018) · Biometrical Journal · doi:10.1002/bimj.201700067 · Cited by 1,673
Guidelines for performing Mendelian randomization investigations
Burgess, S., Davey Smith, G., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Holmes, M. V., Minelli, C., Relton, C. L., et al. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15555.2 · Cited by 1,273
Guidelines for performing Mendelian randomization investigations: update for summer 2023
Burgess, S., Smith, G. D., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Kutalik, Z., Holmes, M. V., Minelli, C., et al. (2023) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15555.3 · Cited by 747
Causal inference and counterfactual prediction in machine learning for actionable healthcare
Prosperi, M., Guo, Y., Sperrin, M., Koopman, J. S., Min, J. S., He, X., Rich, S., Wang, M., Buchan, I. E., & Bian, J. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0197-y · Cited by 459
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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