The Control Outcome Calibration Approach for Causal Inference With Unobserved Confounding
Open accessAbstract
Unobserved confounding can seldom be ruled out with certainty in nonexperimental studies. Negative controls are sometimes used in epidemiologic practice to detect the presence of unobserved confounding. An outcome is said to be a valid negative control variable to the extent that it is influenced by unobserved confounders of the exposure effects on the outcome in view, although not directly influenced by the exposure. Thus, a negative control outcome found to be empirically associated with the exposure after adjustment for observed confounders indicates that unobserved confounding may be present. In this paper, we go beyond the use of control outcomes to detect possible unobserved confounding and propose to use control outcomes in a simple but formal counterfactual-based approach to correct causal effect estimates for bias due to unobserved confounding. The proposed control outcome calibration approach is developed in the context of a continuous or binary outcome, and the control outcome and the exposure can be discrete or continuous. A sensitivity analysis technique is also developed, which can be used to assess the degree to which a violation of the main identifying assumption of the control outcome calibration approach might impact inference about the effect of the exposure on the outcome in view.
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
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?
In that situation, the difference of the negative control results from the null can be used as an indicator of the extent of bias in the original study.
“In that situation, the difference of the negative control results from the null can be used as an indicator of the extent of bias in the original study.41,42”
From Triangulation in aetiological epidemiology (2017) · cites this paper as [42]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793 · see citing works
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 · see citing works
Specification Tests in Econometrics
Hausman, J. A. (1978) · Econometrica · doi:10.2307/1913827 · Cited by 18,820 · see citing works
Negative Controls: A Tool for Detecting Confounding and Bias in Observational Studies
Lipsitch, M., Tchetgen Tchetgen, E., & Cohen, T. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181d61eeb · Cited by 1,583 · see citing works
Self-citationA linear regression model for the analysis of life times
Aalen, O. O. (1989) · Statistics in Medicine · doi:10.1002/sim.4780080803 · Cited by 725 · see citing works
Estimating Exposure Effects by Modelling the Expectation of Exposure Conditional on Confounders
Robins, J. M., Mark, S. D., & Newey, W. K. (1992) · Biometrics · doi:10.2307/2532304 · Cited by 384 · see citing works
Semiparametric estimation of an accelerated failure time model with time-dependent covariates
Robins, J. M., & Tsiatis, A. A. (1992) · Biometrika · doi:10.1093/biomet/79.2.311 · Cited by 138 · see citing works
Methylmercury-induced chromosome damage in man
Skerfving, S., Hansson, K., Mangs, C., et al. (1974) · Environmental Research · doi:10.1016/0013-9351(74)90078-4 · Cited by 101 · see citing works
A Method for Detection of Residual Confounding in Time-series and Other Observational Studies
Flanders, W. D., Klein, M., Darrow, L. A., Strickland, M. J., Sarnat, S., Sarnat, J. A., Waller, L. A., Winquist, A. G., & Tolbert, P. E. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181fdcabe · Cited by 91 · see citing works
Negative Control Exposures in Epidemiologic Studies
Smith, G. D. (2012) · Epidemiology · doi:10.1097/ede.0b013e318245912c · Cited by 88 · see citing works
The Role of Known Effects in Observational Studies
Rosenbaum, P. R. (1989) · Biometrics · doi:10.2307/2531497 · Cited by 68 · see citing works
Negative Control Exposures in Epidemiologic Studies
Lipsitch, M., Tchetgen, E. T., & Cohen, T. (2012) · Epidemiology · doi:10.1097/ede.0b013e3182460c23 · Cited by 16 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [2]Rosenbaum P Observational Studies 2002 2nd ed New York, NY Springer-Verlag
Cited by
Sorted by citations
Triangulation in aetiological epidemiology
Lawlor, D. A., Tilling, K., & Davey Smith, G. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyw314 · Cited by 1,129 · see citing works
When and how can real world data analyses substitute for randomized controlled trials?
Franklin, J. M., & Schneeweiss, S. (2017) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.857 · Cited by 316 · see citing works
Schuemie, M. J., Hripcsak, G., Ryan, P. B., Madigan, D., & Suchard, M. A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1708282114 · Cited by 163 · see citing works
Sanderson, E., Richardson, T. G., Hemani, G., & Davey Smith, G. (2021) · International Journal of Epidemiology · doi:10.1093/ije/dyaa288 · Cited by 115 · see citing works
Richardson, D. B., Laurier, D., Schubauer-Berigan, M. K., Tchetgen, E. T., & Cole, S. R. (2014) · American Journal of Epidemiology · doi:10.1093/aje/kwu211 · Cited by 51 · see citing works
- Authors
- Tchetgen Tchetgen, E.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-12-20
- Volume
- 179
- Issue
- 5
- Pages
- 633-640
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- PMC
- PMC3927977
- OpenAlex
- W2158881916
- Cited by
- 119
- References
- 12
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 9 · 2023: 17 · 2022: 6 · 2021: 12 · 2020: 13 · 2019: 4 · 2018: 5 · 2017: 8 · 2016: 9 · 2015: 7 · 2014: 5
- FWCI
- 6.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- American Journal of Epidemiology (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Harvard University
- National Institutes of Health
- National Institute of General Medical Sciences (GM088558, U54GM088558)
- National Institute of Allergy and Infectious Diseases (AI 51164, R01AI104459, AI104459, R01AI051164, R37AI051164)
- National Institute of Environmental Health Sciences (R01ES020337, R21ES019712, ES019712)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- unmeasured confounding, negative control outcomes, causal effect estimation, counterfactual modeling, bias correction, sensitivity analysis
- MeSH terms
- Humans, Models, Statistical, Bias, Causality, Confounding Factors, Epidemiologic
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
