The DOI matches its Crossref record; 35 of the 36 references it cites were found in a registry or an index.
A New Criterion for Confounder Selection
Open accessAbstract
We propose a new criterion for confounder selection when the underlying causal structure is unknown and only limited knowledge is available. We assume all covariates being considered are pretreatment variables and that for each covariate it is known (i) whether the covariate is a cause of treatment, and (ii) whether the covariate is a cause of the outcome. The causal relationships the covariates have with one another is assumed unknown. We propose that control be made for any covariate that is either a cause of treatment or of the outcome or both. We show that irrespective of the actual underlying causal structure, if any subset of the observed covariates suffices to control for confounding then the set of covariates chosen by our criterion will also suffice. We show that other, commonly used, criteria for confounding control do not have this property. We use formal theory concerning causal diagrams to prove our result but the application of the result does not rely on familiarity with causal diagrams. An investigator simply need ask, "Is the covariate a cause of the treatment?" and "Is the covariate a cause of the outcome?" If the answer to either question is "yes" then the covariate is included for confounder control. We discuss some additional covariate selection results that preserve unconfoundedness and that may be of interest when used with our criterion.
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“In short, there exists no set of pre-treatment controls that is sufficient for identifying the causal effect of T on Y, and hence the VanderWeele & Shpitser (2011) rule does not apply.”
From Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable (2014) · cites this paper as VanderWeele & Shpitser 2011
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“If the analyst is willing to assume that controlling for some combination of observed pre-treatment variables is sufficient to identify the total causal effect of treatment, then it is sufficient to control for all variables that (directly or indirectly) cause treatment or outcome or both (VanderWeele & Shpitser 2011).”
From Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable (2014) · cites this paper as VanderWeele & Shpitser 2011
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Cites
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
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Rosenbaum, P. R., & Rubin, D. B. (1985) · The American Statistician · doi:10.2307/2683903 · Cited by 6,472
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Abadie, A., & Imbens, G. W. (2006) · Econometrica · doi:10.1111/j.1468-0262.2006.00655.x · Cited by 2,908
Bayesian inference for causal effects: The role of randomization
Rubin, D. B. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344064 · Cited by 2,503
Causal diagrams for empirical research
PEARL, J. (1995) · Biometrika · doi:10.1093/biomet/82.4.669 · Cited by 2,355 · cited 13×
“Pearl’s “backdoor path criterion” (Pearl, 1995) provided a simple graphical criterion to assess the adequacy of controlling for a particular covariate set.”
BackgroundMethodResultInfluentialOveradjustment bias and unnecessary adjustment in epidemiologic studies.
Schisterman, E. F., Cole, S. R., & Platt, R. W. (2009) · Epidemiology · doi:10.1097/ede.0b013e3181a819a1 · Cited by 2,096 · cited 1×
“It is well known that control for such variables tends to increase the standard error of the estimates of treatment effects (e.g., Robinson and Jewell, 1991; Schisterman, Cole, and Platt, 2009).”
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Why Propensity Scores Should Not Be Used for Matching
King, G., & Nielsen, R. (2019) · Political Analysis · doi:10.1017/pan.2019.11 · Cited by 1,706
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
Principles of confounder selection
VanderWeele, T. J. (2019) · European Journal of Epidemiology · doi:10.1007/s10654-019-00494-6 · Cited by 1,535
Self-citationEndogenous Selection Bias: The Problem of Conditioning on a Collider Variable
Elwert, F., & Winship, C. (2014) · Annual Review of Sociology · doi:10.1146/annurev-soc-071913-043455 · Cited by 1,168
A Guideline for Reporting Mediation Analyses of Randomized Trials and Observational Studies
Lee, H., Cashin, A. G., Lamb, S. E., Hopewell, S., Vansteelandt, S., VanderWeele, T. J., MacKinnon, D. P., Mansell, G., Collins, P. G. S., Golub, R. M., et al. (2021) · JAMA · doi:10.1001/jama.2021.14075 · Cited by 559
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Metadata
- Authors
- VanderWeele, T. J., & Shpitser, I.
- Affiliations
- Harvard University (US)
- Corresponding author
- VanderWeele, Tyler J.
- Venue
- Biometrics
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-05-31
- Volume
- 67
- Issue
- 4
- Pages
- 1406-1413
- ISSN
- 0006-341X (ISSN-L)
- PMC
- PMC3166439
- OpenAlex
- W2025309870
- Cited by
- 434
- References
- 36
- Citations by year
- 2026: 25 · 2025: 46 · 2024: 33 · 2023: 47 · 2022: 41 · 2021: 53 · 2020: 59 · 2019: 32 · 2018: 17 · 2017: 25 · 2016: 13 · 2015: 14 · 2014: 10 · 2013: 11 · 2012: 4
- FWCI
- 7.85
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Biometrics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institutes of Health
- National Institute of Allergy and Infectious Diseases (R01AI104459)
- National Institute of Environmental Health Sciences (ES017876, R01ES017876, R56ES017876)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R03HD060696, HD060696)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- confounder selection, confounding adjustment, causal graphs, unconfoundedness, covariate selection, causal structure
- MeSH terms
- Algorithms, Biometry, Computer Simulation, Data Interpretation, Statistical, Epidemiologic Methods, Humans, Numerical Analysis, Computer-Assisted, Risk Factors, Models, Statistical, Confounding Factors, Epidemiologic, Confidence Intervals, Statistical Distributions, Proportional Hazards Models, Outcome Assessment, Health Care, Risk Assessment
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