Academic Journal
Empirically Assessing the Plausibility of Unconfoundedness in Observational Studies.
| Title: | Empirically Assessing the Plausibility of Unconfoundedness in Observational Studies. |
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| Authors: | Hartwig FP; From the Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil.; MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom., Tilling K; MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom.; Population Health Sciences, University of Bristol, Bristol, United Kingdom., Davey Smith G; MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom.; Population Health Sciences, University of Bristol, Bristol, United Kingdom. |
| Source: | Epidemiology (Cambridge, Mass.) [Epidemiology] 2026 Jul 01; Vol. 37 (4), pp. 515-522. Date of Electronic Publication: 2026 Apr 07. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 9009644 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1531-5487 (Electronic) Linking ISSN: 10443983 NLM ISO Abbreviation: Epidemiology Subsets: MEDLINE |
| Imprint Name(s): | Publication: <2000>- : Hagerstown, MD : Lippincott Williams & Wilkins Original Publication: [Cambridge, MA : Blackwell Scientific Publications ; Chestnut Hill, MA : Epidemiology Resources, c1990- |
| MeSH Terms: | Observational Studies as Topic*/methods , Causality*, Humans ; Confounding Factors, Epidemiologic |
| Abstract: | The possibility of unmeasured confounding is one of the main limitations for causal inference from observational studies. There are different methods for (partially) empirically assessing the plausibility of unconfoundedness. However, most currently available methods require (at least partial) assumptions about the confounding structure, which may be difficult to know in practice. In this paper, we describe a simple strategy for empirically assessing the plausibility of conditional unconfoundedness (i.e., whether the candidate adjustment set of covariates suffices for confounding adjustment), which does not require any explicit assumptions about the confounding structure, relying instead on assumptions related to temporal ordering between covariates, exposure, and outcome (which can be guaranteed by design) and selection into the study. The proposed method essentially relies on testing the association between a subset of the covariates included in the adjustment set (those associated with the exposure, given all other covariates) and the outcome conditional on the remaining covariates and the exposure. We describe the assumptions underlying the method, provide proofs, use simulations to corroborate the theory, and illustrate the method with an applied example assessing the causal effect of delivery mode and intelligence quotient measured in adulthood using data from the 1982 Pelotas (Brazil) birth cohort. We also discuss the implications of measurement error and some important limitations of the suggested approach. (Copyright © 2026 Wolters Kluwer Health, Inc. All rights reserved.) |
| Competing Interests: | Disclosure: The authors report no conflicts of interest. |
| References: | Hernán MA, Robins JM. Causal Inference: What If. Chapman & Hall/CRC; 2020. Davey Smith G, Phillips AN. Correlation without a cause: an epidemiological odyssey. Int J Epidemiol. 2020;49:4–14. Davey Smith G, Holmes MV, Davies NM, Ebrahim S. Mendel’s laws, Mendelian randomization and causal inference in observational data: substantive and nomenclatural issues. Eur J Epidemiol. 2020;35:99–111. Davey Smith G, Ebrahim S. Mendelian randomization: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32:1–22. Hernan MA, Alonso A, Logan R, et al. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease. Epidemiology. 2008;19:766–779. Pearl J. Causality: Models, Inference and Reasoning. 2nd ed. Cambridge University Press; 2009. Gutierrez S, Glymour MM, Smith GD. Evidence triangulation in health research. Eur J Epidemiol. 2025;40:743–757. Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015;34:3661–3679. Baum CF, Schaffer ME, Stillman S. Instrumental variables and GMM: estimation and testing. Stata J. 2003;3:1–31. Smith GD. Negative control exposures in epidemiologic studies. Epidemiology. 2012;23:350–1; author reply 351-2. Lipsitch M, Tchetgen Tchetgen E, Cohen T. Negative controls: a tool for detecting confounding and bias in observational studies. Epidemiology. 2010;21:383–388. Entner D, Hoyer P, Spirtes P. Data-driven covariate selection for nonparametric estimation of causal effects. In: Carlos MC, Pradeep R, eds. Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics; Proceedings of Machine Learning Research: PMLR; 2013:256–64. Barry C, Liu J, Richmond R, et al. Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data. PLoS Genet. 2021;17:e1009703. Cai J, Zhang N, Zhou X, Spiegelman D, Wang M. Correcting for bias due to mismeasured exposure history in longitudinal studies with continuous outcomes. Biometrics. 2023;79:3739–3751. Keogh RH, White IR. A toolkit for measurement error correction, with a focus on nutritional epidemiology. Stat Med. 2014;33:2137–2155. Victora CG, Barros FC. Cohort profile: the 1982 Pelotas (Brazil) birth cohort study. Int J Epidemiol. 2006;35:237–242. Horta BL, Gigante DP, Goncalves H, et al. Cohort profile update: the 1982 Pelotas (Brazil) Birth Cohort Study. Int J Epidemiol. 2015;44:441–441e. Barros AJD, Victora CG, Horta BL, et al.; Pelotas Cohorts Study Group. Antenatal care and caesarean sections: trends and inequalities in four population-based birth cohorts in Pelotas, Brazil, 1982-2015. Int J Epidemiol. 2019;48(Suppl 1):i37–i45. Victora CG, Hartwig FP, Vidaletti LP, et al. Effects of early-life poverty on health and human capital in children and adolescents: analyses of national surveys and birth cohort studies in LMICs. Lancet. 2022;399:1741–1752. Barros AJ, Victora CG, Horta BL, Goncalves HD, Lima RC, Lynch J. Effects of socioeconomic change from birth to early adulthood on height and overweight. Int J Epidemiol. 2006;35:1233–1238. Schreck N, Slynko A, Saadati M, Benner A. Statistical plasmode simulations-Potentials, challenges and recommendations. Stat Med. 2024;43:1804–1825. Myers JA, Rassen JA, Gagne JJ, et al. Effects of adjusting for instrumental variables on bias and precision of effect estimates. Am J Epidemiol. 2011;174:1213–1222. Ding P, VanderWeele TJ, Robins JM. Instrumental variables as bias amplifiers with general outcome and confounding. Biometrika. 2017;104:291–302. VanderWeele TJ. Principles of confounder selection. Eur J Epidemiol. 2019;34:211–219. VanderWeele TJ, Shpitser I. A new criterion for confounder selection. Biometrics. 2011;67:1406–1413. Guo FR, Zhao Q. Confounder selection via iterative graph expansion. Ann. Statist. 2026;54:516–541. Brodeur A, Cook N, Hartley J, Heyes A. Do Preregistration and Preanalysis Plans Reduce p-Hacking and Publication Bias? Evidence from 15,992 Test Statistics and Suggestions for Improvement. JPE Micro. 2024;2:527–561. Dirnagl U. Preregistration of exploratory research: learning from the golden age of discovery. PLoS Biol. 2020;18:e3000690. Perkins NJ, Cole SR, Harel O, et al. Principled approaches to missing data in epidemiologic studies. Am J Epidemiol. 2018;187:568–575. Seaman SR, White IR. Review of inverse probability weighting for dealing with missing data. Stat Methods Med Res. 2013;22:278–295. Sterne JA, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393. Harel O, Mitchell EM, Perkins NJ, et al. Multiple imputation for incomplete data in epidemiologic studies. Am J Epidemiol. 2018;187:576–584. Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiological epidemiology. Int J Epidemiol. 2016;45:1866–1886. |
| Contributed Indexing: | Keywords: Backdoor path; Bias; Causal inference; Confounding; Directed acyclic graphs; Observational studies |
| Entry Date(s): | Date Created: 20260407 Date Completed: 20260714 Latest Revision: 20260714 |
| Update Code: | 20260715 |
| DOI: | 10.1097/EDE.0000000000001985 |
| PMID: | 41944641 |
| Database: | MEDLINE |
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