Cited by 434Advanced Causal Inference TechniquesStatistical Methods and InferenceBayesian Modeling and Causal Inference

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A New Criterion for Confounder Selection

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VanderWeele, Tyler J., Shpitser, Ilya·2011·Biometrics

DOI 10.1111/j.1541-0420.2011.01619.xPMC PMC3166439

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Abstract

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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ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · Journal of Educational Psychology · doi:10.1037/h0037350 · Cited by 9,436
Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score
Rosenbaum, P. R., & Rubin, D. B. (1985) · The American Statistician · doi:10.2307/2683903 · Cited by 6,472
Causation, Prediction, and Search
Spirtes, P., Glymour, C., & Scheines, R. (1993) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2748-9 · Cited by 4,617
Graphical Models
Lauritzen, S. L. (1996) · doi:10.1093/oso/9780198522195.001.0001 · Cited by 2,976
Large sample properties of matching estimators for average treatment effects
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.”
BackgroundMethodResultInfluential
Overadjustment 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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Authors: VanderWeele, T. J., & Shpitser, I.
Affiliations: Harvard University (US)
Corresponding author: VanderWeele, Tyler J.
ORCID: VanderWeele, Tyler J.
Shpitser, Ilya
Venue: Biometrics
Publisher: Oxford University Press (OUP)
Published: 2011-05-31
Volume: 67
Issue: 4
Pages: 1406-1413
ISSN: 0006-341X (ISSN-L)
DOI: 10.1111/j.1541-0420.2011.01619.x
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
Licence: doi.wiley.com/10.1002/tdm_license_1.1 (tdm, 1554 d)
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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A New Criterion for Confounder Selection

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