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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
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
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
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.”
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).”
Background
Cited by
Sorted by citations
Items 301–400 of 432
Estimating possible causal effects with latent variables via adjustment and novel rule orientation
Wang, T.-Z., Tao, L., Qin, T., & Zhou, Z. (2025) · Artificial Intelligence · doi:10.1016/j.artint.2025.104387 · Cited by 2
Paredes-Cruz, M., Pérez-Morán, D., Doubova, S. V., Arsenault, C., & Fresnedo, C. Q. (2025) · PLoS ONE · doi:10.1371/journal.pone.0323039 · Cited by 2
Chong, G. Y., Kaur, S., Talib, R. A., Loy, S., Tan, H., Abdullah, R. B., Mahmud, H. B., Siah, W. Y., Kee, C., & Koo, H.-C. (2025) · Journal of Sleep Research · doi:10.1111/jsr.70218 · Cited by 2
Rosolen, V., Barbiero, F., Mariuz, M., Parpinel, M. T., Ronfani, L., Brumatti, L. V., Bin, M., Castriotta, L., Valent, F., Little, D., et al. (2025) · BMJ Nutrition Prevention & Health · doi:10.1136/bmjnph-2025-001313 · Cited by 2
Repeated exposure to psychosocial work-related factors and arterial stiffness among aging workers
Massamba, V. K., Milot, A., Talbot, D. C., Gilbert‐Ouimet, M., Brisson, C., & Trudel, X. (2025) · International Archives of Occupational and Environmental Health · doi:10.1007/s00420-025-02177-0 · Cited by 2
Govia, I., Doubova, S. V., Younger-Coleman, N. O., & Atkinson, U. (2021) · Journal of Addictions Nursing · doi:10.1097/jan.0000000000000430 · Cited by 3
Doubova, S. V., Ortiz‐Panozo, E., & Pérez‐Cuevas, R. (2022) · Maternal and Child Health Journal · doi:10.1007/s10995-022-03504-4 · Cited by 3
Neighborhood-based cross fitting approach to treatment effects with high-dimensional data
Agboola, O. D., & Yu, H. (2023) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2023.107780 · Cited by 3
Meisinger, C., Kirchberger, I., Raake, P. W. J., Linseisen, J., & Schmitz, T. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12196349 · Cited by 2
Skobic, I., Pezza, M., Howe, G. W., & Haynes, P. L. (2023) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2023.111585 · Cited by 3
Mathur, M. B., VanderWeele, T. J., & Shpitser, I. (2025) · The Annals of Statistics · doi:10.1214/24-aos2448 · Cited by 2
Self-citationMeyer-Baron, M., Schäper, M., & van Thriel, C. (2014) · NeuroToxicology · doi:10.1016/j.neuro.2014.03.002 · Cited by 2
Causal Structure Learning and Effect Identification in Linear Non-Gaussian Models and Beyond
Entner, D. (2013) · Cited by 2
Dantan, É., Leborgne, F., Giral, M., Dion, A., Querard, A.-H., & Foucher, Y. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.02.007 · Cited by 2
What Can the Millions of Random Treatments in Nonexperimental Data Reveal About Causes?
Ribeiro, A. F., Neffke, F. M. H., & Hausmann, R. (2022) · SN Computer Science · doi:10.1007/s42979-022-01319-2 · Cited by 2
Adjustment with three continuous variables
Knaeble, B. (2019) · Communications in statistics. Simulation and computation · doi:10.1080/03610918.2017.1390128 · Cited by 2
Amoon, A. T., Crespi, C. M., Nguyen, A. M., Zhao, X., Vergara, X. P., Arah, O. A., & Kheifets, L. (2020) · Cancer Causes & Control · doi:10.1007/s10552-020-01299-9 · Cited by 2
Discussion of “Data-driven Confounder Selection via Markov and Bayesian Networks” by Jenny Häggström
Kennedy, E. H., & Balakrishnan, S. (2017) · Biometrics · doi:10.1111/biom.12787 · Cited by 2
Geraldo, P., & Brand, J. E. (2020) · Oxford University Press eBooks · doi:10.1093/obo/9780199756384-0240 · Cited by 2
Olmstead, A. D., Nikiforuk, A. M., Schwartz, S., Márquez, A. C., Valadbeigy, T., Flores, E., Saran, M., Goldfarb, D. A., Hayden, A. S., Masud, S., et al. (2022) · medRxiv · doi:10.1101/2022.09.28.22280429 · Cited by 2
Linking Physical Violence to Women's Mobility in ChilePreprint
Contreras, H., Candia, C., Olchevskaia, R. V. T., Ferres, L., Celedón, M. L. B., Lepri, B., & Rodríguez‐Sickert, C. (2023) · doi:10.31235/osf.io/uad59 · Cited by 2
Ikeda, S., Shibanuma, A., Pokharel, A., Silwal, R. C., & Jimba, M. (2023) · PLOS Global Public Health · doi:10.1371/journal.pgph.0002101 · Cited by 1
del Campo-Albendea, L., Viñuela, A. G. D. L. S., Peñuelas, Ó., Zubizarreta, J. I. P., Khan, K. S., Muriel, A., & Zamora, J. (2025) · Medicina Intensiva · doi:10.1016/j.medine.2025.502142 · Cited by 1
The effects of the COVID-19 pandemic on wellbeing up to 2.5 years from its outbreak in Switzerland
Gondek, D., Voorpostel, M., Mira, N. S., & Mehmeti, T. (2025) · Journal of Public Health · doi:10.1093/pubmed/fdaf107 · Cited by 1
Alanis-Ocádiz, A., Doubova, S. V., Arreola-Guerra, J. M., Monroy, A., Martínez-Bautista, H., Padilla-López, J., Quiñones-Villalobos, C., & Prado-Aguilar, C. A. (2025) · BMC Public Health · doi:10.1186/s12889-025-24526-x · Cited by 1
Chong, G. Y., Kaur, S., Talib, R. A., Loy, S. L., Tan, H. Y., Siah, W. Y., Mazri, F. H., & Koo, H. C. (2025) · Biological Rhythm Research · doi:10.1080/09291016.2025.2573691 · Cited by 1
Review of Quasi-Randomization Approaches for Estimation from Nonprobability Samples
Beresovsky, V., Gershunskaya, J., & Savitsky, T. D. (2026) · Statistical Science · doi:10.1214/24-sts950 · Cited by 1
Wirler, J., Stein, M. J., Baurecht, H., Bohmann, P., Freisling, H., Lendt, C., Sedlmeier, A. M., & Leitzmann, M. F. (2026) · BMC Medicine · doi:10.1186/s12916-026-04907-8 · Cited by 1
Lanas, F. T., Alcocer-Gamba, M. A., Narváez, C. A., Aguayo, R., López, P. L., Verdejo, H., Riesgo, J., Levy, F., Doubova, S. V., Pérez‐Cuevas, R., et al. (2026) · BMC Cardiovascular Disorders · doi:10.1186/s12872-026-05924-4 · Cited by 1
Gillikin, A. P., Lee, Y., Varney, C., Dang, J. T., Tsung, A., Nimeri, A. A., & Shin, T. H. (2026) · EClinicalMedicine · doi:10.1016/j.eclinm.2026.104033 · Cited by 2
The Power of Prognosis: Improving Covariate Balance Tests with Outcome Information
Bicalho, C., Bouyamourn, A. Z. A., & Dunning, T. (2026) · Political Analysis · doi:10.1017/pan.2026.10050 · Cited by 5
Causal Inference and Machine Learning for Reliable Decision-Making
Rasmi, M. (2026) · Eduschool International Journal of Data Science and Machine learning (EIJDSML) · doi:10.63090/eijdsml/3139.7344.0006 · Cited by 1
Smith, E. G. (2014) · F1000Research · doi:10.12688/f1000research.4801.1 · Cited by 1
Collection of Biostatistics Research Archive
VanderWeele, T. J., & Shpitser, I. (2011) · Cited by 1
Self-citationA Visual Diagnostic Tool for Causal Inference
McGowan, L. D., & D'Agostino, R. B. (2023) · Observational Studies · doi:10.1353/obs.2023.0008
Confounder selection via iterative graph expansion
Guo, F. R., & Zhao, Q. (2026) · The Annals of Statistics · doi:10.1214/25-aos2577 · Cited by 9
de Jonge, S. W., Hulskes, R. H., Nikoo, M. Z., Weenink, R. P., Meyhoff, C. S., Leslie, K., Myles, P. S., Forbes, A. B., Greif, R. T., Akça, O., et al. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-067243 · Cited by 1
Practically effective adjustment variable selection in causal inference
Noda, A., & Isozaki, T. (2025) · Journal of Physics Complexity · doi:10.1088/2632-072x/ada861 · Cited by 1
Islam, M. S., Shikalgar, S., & Noor‐E‐Alam, M. (2025) · IISE Transactions on Healthcare Systems Engineering · doi:10.1080/24725579.2024.2447715 · Cited by 3
Association Between Rewarming Rate and Survival and Neurologic Outcome of Accidental Hypothermia
Hara, C., Taira, T., Inoue, A., Nishimura, T., Kikuta, S., Yamamoto, N., Ijuin, S., Takauji, S., Hayakawa, M., & Ishihara, S. (2025) · Critical Care Medicine · doi:10.1097/ccm.0000000000006712 · Cited by 1
Fundamental concepts for causal inference in medicine
Tanaka, S. (2019) · Japanese Journal of Biometrics · doi:10.5691/jjb.40.35 · Cited by 1
Learning Adjustment Sets from Observational and Limited Experimental Data
Triantafillou, S., & Cooper, G. F. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i11.17194 · Cited by 1
Multiple sclerosis hospitalizations among users of oral disease-modifying therapies
Nguyen, T. P. P., Jacobs, D. A., Thibault, D. P., & Willis, A. W. (2021) · Multiple Sclerosis and Related Disorders · doi:10.1016/j.msard.2021.102944 · Cited by 1
Authors’ reply to Yamana and colleagues
Tanaka, S., & Kawakami, K. (2015) · BMJ · doi:10.1136/bmj.h6761 · Cited by 1
Regarding the Relationship Between 48-Hour Fluid Balance and Acute Kidney Injury
Hatton, G. E., Harvin, J. A., Wade, C. E., & Kao, L. S. (2020) · Journal of the American College of Surgeons · doi:10.1016/j.jamcollsurg.2020.02.023 · Cited by 1
Finding Valid Adjustments under Non-ignorability with Minimal DAG KnowledgePreprint
Shah, A., Shanmugam, K., & Ahuja, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.11560 · Cited by 1
Islam, S., & Noor-E-Alam (2021) · arXiv (Cornell University) · Cited by 1
Causal Impact Analysis of Enhanced Phosphorus Effluent Standard on River Water QualityPreprint
Chung, S., & Kim, S. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4127249 · Cited by 1
Robust Covariate Selection for Doubly Robust Estimators in Causal InferencePreprint
Steiner, P. M., & Lyu, W. (2024) · doi:10.31234/osf.io/rsy5e · Cited by 1
On Associative Confounder BiasPreprint
Wijayatunga, P. D. C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.07941
Causal inference in high-dimensional systems
Colombo, D. (2013) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-010025947
On the Structural Causal Model
Kuroki, M., & Kobayashi, F. (2012) · Japanese Journal of Biometrics · doi:10.5691/jjb.32.119
[Thomas Platter (1499?-1582), seen by an universitary member and a doctor].
Sasse, D. (2004) · PubMed
Using the Literature to Identify ConfoundersPreprint
Malec, S. A. (2018) · EasyChair preprint · doi:10.29007/zj61
Causal inference to detect selection bias in road safety epidemiologyPreprint
Dufournet, M., Lanoy, É., Martin, J., & Viallon, V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.03775
Causal Inference in HIV/AIDS Research: Generalizability and Applications
Buchanan, A. L. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/pb3s-xr78
Haapaniemi, A., Mäkitie, A. A., Kekolahti, P., & Ryynänen, O. (2019) · Oral Oncology · doi:10.1016/j.oraloncology.2019.09.007
G-computation and inverse probability weighting for time-to-event outcomes: a comparative studyPreprint
Chatton, A., Leborgne, F., Leyrat, C., & Foucher, Y. (2020) · arXiv (Cornell University)
On Associative Confounder Bias
Wijayatunga, P. D. C. (2015) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-589-0-157
Pressat-Laffouilhère, T., Massonnaud, C., Bréard, H., Lefebvre, M., Falissard, B., & Gillibert, A. (2021) · Research Square · doi:10.21203/rs.3.rs-591022/v1
Differential Response Policy and Child Maltreatment Re-report
Hunter, A. A. (2017) · doi:10.33915/etd.5844
Caregiver mental health status as a predictor of unplanned older adult rehospitalizations
Nasrin, S. (2021) · eScholarship@McGill (McGill) · doi:10.82308/37053
Drug safety and effectiveness in relation to kidney function : a pharmacoepidemiological approach
Trevisan, M. (2021) · Figshare
A Barometer for Mormon Social Science
Bell, R. (2019) · Dialogue A Journal of Mormon Thought · doi:10.5406/dialjmormthou.52.4.0163
Heinze, G., Wallisch, C., & Dunkler, D. (2019) · Biometrical Journal · doi:10.1002/bimj.201900196
Lijing Lin, A., Poppe, K., Wood, A., Martin, G. P., Peek, N. B., & Sperrin, M. (2022) · Research Square · doi:10.21203/rs.3.rs-1824359/v1
Possession of Low Vision Reading Devices Among Residents Living in a Retirement Facility
Shepherd, J. L., Vice, J. E., Shultz, H. L., Davis, M. M., Hseih, J., Yuen, H. K., & Vogtle, L. K. (2022) · Journal of Visual Impairment & Blindness · doi:10.1177/0145482x221105891
Lijing Lin, A., Poppe, K., Wood, A., Martin, G. P., Peek, N. B., & Sperrin, M. (2022) · Research Square · doi:10.21203/rs.3.rs-1824359/v2
Confounding and Confounding ControlPreprint
Ren, S., & Zhao, G. (2022) · Research Square · doi:10.21203/rs.3.rs-2384549/v1
GlobeCorr: interactive globe-based visualization for correlation datasets
Arab, M., Woods, N., Garlock, E. S., Winsor, G. L., Parks, J., Jia, B., Doiron, D., Takaro, T. K., Brook, J. R., & Brinkman, F. S. L. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbac099
Personalized Interventions to Increase the Employment Success of People With Disability
Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2023) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2023.3291547
Eilenberg, W. H., Waduud, M. A., Davies, H., Bailey, M., Scott, D. J. A., Wolf, F., Sotir, A., Lakowitsch, S., Kaider, A., Heinze, G., et al. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1219744
False Discovery Rate Control for Confounder Selection Using Mirror Statistics
Harada, K., & Taguri, M. (2025) · Statistics in Medicine · doi:10.1002/sim.70116
Observational Causality Testing
Knaeble, B., Osting, B., & Tshiaba, P. (2024) · Stat · doi:10.1002/sta4.70017
Unseld, T., Ickstadt, K., Ward, K. C., Switchenko, J. M., Chang, H. H., & Hüls, A. (2024) · Spatial and Spatio-temporal Epidemiology · doi:10.1016/j.sste.2024.100698
Rele, S., Schilling, C. G., Shadbolt, C., Spelman, T. D., Taylor, N. F., Dowsey, M. M., & Choong, P. (2024) · ANZ Journal of Surgery · doi:10.1111/ans.19322
Pérez-Araluce, R., Bes‐Rastrollo, M., Gea, A., Martínez‐González, M. Á., VanderWeele, T. J., & Chen, Y. (2024) · Preventive Medicine · doi:10.1016/j.ypmed.2024.108209
Self-citationRe-evaluating causal inference: Bias reduction in confounder-effect modifier scenarios
Wang, X., Oraby, T. F., Mao, X., Sun, G., & Schneider, H. (2025) · Decision Support Systems · doi:10.1016/j.dss.2025.114435
Words That Matter: Analyzing the Causal Effect of WordsPreprint
Lemaire, A., Yin, M., & Netzer, O. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5205681 · Cited by 1
Zaytseva, A., Verger, P., & Ventelou, B. (2025) · Health Services Insights · doi:10.1177/11786329251331128
Klassische Methoden der Variablenselektion
Heinze, G., Wallisch, C., Schmid, M. C., & Dunkler, D. (2023) · Springer eBooks · doi:10.1007/978-3-662-63496-7_10-1
Program for Psychosocial Assessment During Pregnancy: Protocol for a Multimethod Study
Lindahl, Y.-L., Norén, H., Skalkidou, A., & Åslund, C. (2025) · JMIR Research Protocols · doi:10.2196/67179
Bryant, Z., Morley, K. C., Kerr, J. A., Olsson, C. A., & Slade, T. N. (2025) · Addiction · doi:10.1111/add.70173
Intravenous methocarbamol for acute pain after spine surgery: a target trial emulation
Potnuru, P., Baranov, A., Khudirat, M., & Turan, A. (2025) · Regional Anesthesia & Pain Medicine · doi:10.1136/rapm-2025-107010
Lolong, D. B., Irianto, J., Kusrini, I., Tarigan, I. U., Letelay, A. M., Tobing, K., Herawati, M. H., Pracoyo, N. E., Isfandari, S., Raflizar, et al. (2025) · Asian Pacific Journal of Tropical Medicine · doi:10.4103/apjtm.apjtm_80_25
Márquez-Romero, J. M., Doubova, S. V., Bonifacio-Delgadillo, D. M., Zavala, Á. E. M., & Salinas-Gutiérrez, R. (2025) · Brain Circulation · doi:10.4103/bc.bc_28_25
Dietary Starch and Its Relationship with Caries Progression: Long-Term Findings from Finland
Yamanaka, S., Yedjou, C. G., Duhan, A., & Venugopal, A. (2025) · Turkish journal of Public Health Dentistry · doi:10.51847/ojcnt3vys3
Doubova, S. V., Paredes-Cruz, M., Pérez-Morán, D., Cortes, L. R. G., Espinoza‐Anrubio, G., Ruiz, C., Cerda-Mancillas, M. C., Aguilar, C. A. P., Garcia, M. Á. R., Delgado, A. S., et al. (2025) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-025-08301-9
Rees, S., Nie, J., Yue, Y., Wactawski‐Wende, J., Patel, S. P., Andrews, C., Wallace, R. B., Gower, E. W., & Millen, A. E. (2025) · Ophthalmology Science · doi:10.1016/j.xops.2025.100994
Gopang, M., Babalola, T. K., Fruh, V., Sears, C. G., Harrington, J. M., Téllez-Plaza, M., Webster, T. F., Tjønneland, A. M., Mann, K. K., Wellenius, G. A., et al. (2025) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2025.180783
Patterson, W. B., Glasson, J., Naik, N., Jones, R. B., Berger, P. K., Plows, J. F., Minor, H. A., Lurmann, F., Goran, M. I., & Alderete, T. L. (2021) · Figshare · doi:10.6084/m9.figshare.14738811
Ceretti, E., Donato, F., Zani, C., Villarini, M., Verani, M., De Donno, A., Bonetta, S., Feretti, D., Carducci, A., Idolo, A., et al. (2020) · Figshare · doi:10.6084/m9.figshare.12269624
Patterson, W. B., Glasson, J., Naik, N., Jones, R. B., Berger, P. K., Plows, J. F., Minor, H. A., Lurmann, F., Goran, M. I., & Alderete, T. L. (2021) · Figshare · doi:10.6084/m9.figshare.14738817
Patterson, W. B., Glasson, J., Naik, N., Jones, R. B., Berger, P. K., Plows, J. F., Minor, H. A., Lurmann, F., Goran, M. I., & Alderete, T. L. (2021) · Figshare · doi:10.6084/m9.figshare.14738814
Mellado-Artigas, R., Ferreyro, B. L., Angriman, F., Hernández-Sanz, M. L., Arruti, E., Torres, A., Villar, J., Brochard, L. J., & Ferrando, C. (2021) · Figshare · doi:10.6084/m9.figshare.13940040
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
