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A New Approach To Causal Inference in Mortality Studies With a Sustained Exposure Period - Application To Control of the Healthy Worker Survivor Effect
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“the biostatistics literature has focused on sequential random ignorability assumptions that impose that treatment in each period is random conditional on the path of covariates and realized outcomes, rather than parallel trends.”
From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Robins (1986)
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“Note also that the truncated factorization formula is not restricted to interventions on a single variable; it is applicable to simultaneous or sequential interventions such as those invoked in the analysis of time varying treatment with time varying confounders (Robins, 1986; Arjas and Parner, 2004).”
From Causal inference in statistics: An overview (2009) · cites this paper as Robins, 1986
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“Note also that the truncated factorization formula is not restricted to interventions on a single variable; it is applicable to simultaneous or sequential interventions such as those invoked in the analysis of time varying treatment with time varying confounders (Robins, 1986, Arjas andParner, 2004).”
From An Introduction to Causal Inference (2010) · cites this paper as Robins, 1986
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“the algebraic machinery offered by the counterfactual notation, Y x (u), once a problem is properly formalized, can be extremely powerful in refining assumptions (Angrist et al., 1996, Heckman andVytlacil, 2005), deriving consistent estimands (Robins, 1986), bounding probabilities of necessary and sufficient causation (Tian and Pearl, 2000), and combining data from experimental and nonexperimental”
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“They are assumed to be connected to observed variables via consistency constraints (Robins, 1986) such as X = x =⇒ Y x = Y,(32)”
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“Equitable vaccination coverage requires higher levels of coverage among those most at risk at the intersection of SARS-CoV-2 exposures, severity, and onward transmission(44-48).”
From Social inequalities in COVID-19 death by area-level income in 11.2 million people in Ontario, Canada: patterns over time and the mediating role of vaccination. (2026) · cites this paper as [44]
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Citations
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
The Statistical Analysis of Failure Time Data.
Aitkin, M. A., Kalbfleisch, J. D., & Prentice, R. L. (1981) · Biometrics · doi:10.2307/2530581 · Cited by 9,875
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
Conditional Independence in Statistical Theory
Dawid, A. P. (1979) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1979.tb01052.x · Cited by 1,507
Information and Exponential Families in Statistical Theory.
Kingmán, J. F. C., & Barndorff-Nielsen, O. E. (1979) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344658 · Cited by 1,188
The Consquences of Adjustment for a Concomitant Variable That Has Been Affected by the Treatment
Rosenbaum, P. R. (1984) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2981697 · Cited by 849
Multiplicative Models and Cohort Analysis
Breslow, N. E., Lubin, J. H., Marek, P. M., & Langholz, B. M. (1983) · Journal of the American Statistical Association · doi:10.1080/01621459.1983.10477915 · Cited by 566
CONFOUNDING: ESSENCE AND DETECTION1
Miettinen, O. S., & Cook, E. F. (1981) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a113225 · Cited by 350
Arsenic and Respiratory Cancer in Man: An Occupational Study
Lee, A. M., & Fraumeni, J. F. (1969) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/42.6.1045 · Cited by 253
General Relative-Risk Models for Survival Time and Matched Case-Control Analysis
Thomas, D. C. (1981) · Biometrics · doi:10.2307/2530149 · Cited by 183
Research works printed but not linked: 5
- Robins, 1984, A statistical method to control for the healthy worker effect in intracohort comparisons, Am. J. Epidemiology, 120, 465
- Robins, 1985, A new theory of causality in observational survival studies—Application to the healthy worker effect, Biometrics, 41, 331
- Robins, 1986, Some theorems relating to causal tree graphs
- Thomas, 1977, Methods of cohort analysis. Appraisal by application to asbestos mining (with discussion), J. R. Statist. Soc., A140, 469
- Breslow, 1972, Contribution to discussion of paper by D.R. Cox, J. R. Statist. Soc., B34, 216
Cited by
Sorted by citations
Items 301–400 of 1,004
Causal inference in epidemiological studies with strong confounding
Moore, K. L., Neugebauer, R. S., van der Laan, M. J., & Tager, I. B. (2012) · Statistics in Medicine · doi:10.1002/sim.4469 · Cited by 83
Graphical Models for Causation, and the Identification Problem
Freedman, D. A. (2004) · Evaluation Review · doi:10.1177/0193841x04266432 · Cited by 83
Sequential Causal Models for the Evaluation of Labor Market Programs
Lechner, M. (2009) · Journal of Business and Economic Statistics · doi:10.1198/jbes.2009.0006 · Cited by 83
Mark, S. D., & Robins, J. M. (1993) · Statistics in Medicine · doi:10.1002/sim.4780121707 · Cited by 83
Self-citationHypothetical Midlife Interventions in Women and Risk of Type 2 Diabetes
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Insights into the Cross-world Independence Assumption of Causal Mediation Analysis
Andrews, R. M., & Didelez, V. (2020) · Epidemiology · doi:10.1097/ede.0000000000001313 · Cited by 82
Direct and Indirect Effects for Neighborhood-Based Clustered and Longitudinal Data
VanderWeele, T. J. (2010) · Sociological Methods & Research · doi:10.1177/0049124110366236 · Cited by 84
Cole, S. R., Hudgens, M. G., Brookhart, M. A., & Westreich, D. J. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwv001 · Cited by 82
Bayesian Nonparametric Estimation for Dynamic Treatment Regimes With Sequential Transition Times
Xu, Y., Müller, P., Wahed, A. S., & Thall, P. F. (2015) · Journal of the American Statistical Association · doi:10.1080/01621459.2015.1086353 · Cited by 81
Campbell, K. A., Colacino, J. A., Puttabyatappa, M., Dou, J., Elkin, E. R., Hammoud, S. S., Domino, S. E., Dolinoy, D. C., Goodrich, J. M., Loch-Caruso, R. K., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04623-6 · Cited by 84
Analysis of Longitudinal Data with Drop-Out: Objectives, Assumptions and a Proposal
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Zhao, Y., Laber, E. B., Ning, Y., Saha, S., & Sands, B. E. (2019) · PubMed · doi:10.48550/arxiv.1901.00663 · Cited by 80
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Didelez, V., Kreiner, S., & Keiding, N. (2010) · Statistical Science · doi:10.1214/10-sts340 · Cited by 80
Rosenblum, M., & van der Laan, M. J. (2009) · Biometrics · doi:10.1111/j.1541-0420.2008.01177.x · Cited by 83
Bica, I., Alaa, A. M., Jordon, J., & van der Schaar, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04083 · Cited by 125
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Freedman, D. A., & Humphreys, P. W. (1999) · Synthese · doi:10.1023/a:1005277613752 · Cited by 79
Zheng, W., & van der Laan, M. J. (2017) · Journal of Causal Inference · doi:10.1515/jci-2016-0006 · Cited by 83
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Brown, D., Picciotto, S., Costello, S., Neophytou, A. M., Izano, M. A., Ferguson, J. M., & Eisen, E. A. (2017) · Current Environmental Health Reports · doi:10.1007/s40572-017-0156-x · Cited by 78
Effect of Physical Activity on Functional Performance and Knee Pain in Patients With Osteoarthritis
Mansournia, M. A., Danaei, G., Forouzanfar, M. H., Mahmoodi, M., Jamali, M., Mansournia, N., & Mohammad, K. (2012) · Epidemiology · doi:10.1097/ede.0b013e31824cc1c3 · Cited by 78
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Robins, J. M., & Richardson, T. S. (2010) · Cited by 78
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Mediation analysis for a survival outcome with time‐varying exposures, mediators, and confounders
Lin, S., Young, J. G., Logan, R. W., & VanderWeele, T. J. (2017) · Statistics in Medicine · doi:10.1002/sim.7426 · Cited by 84
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Tchetgen, E. J. T., Fulcher, I., & Shpitser, I. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1811098 · Cited by 78
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Bind, M. (2019) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040218-044048 · Cited by 82
Observations on the dose-response curve for arsenic exposure and lung cancer.
Hertz‐Picciotto, I., & Smith, A. H. (1993) · Scandinavian Journal of Work Environment & Health · doi:10.5271/sjweh.1480 · Cited by 78
Causal Inference for a Population of Causally Connected Units
van der Laan, M. J. (2014) · Journal of Causal Inference · doi:10.1515/jci-2013-0002 · Cited by 78
Siebert, U., Rothenbacher, D., Daniel, U., & Brenner, H. (2001) · Occupational and Environmental Medicine · doi:10.1136/oem.58.12.774 · Cited by 77
Diaz, I. M. (2019) · Biostatistics · doi:10.1093/biostatistics/kxz042 · Cited by 76
Estimating heterogeneous survival treatment effect in observational data using machine learning
Hu, L., Ji, J., & Li, F. (2021) · Statistics in Medicine · doi:10.1002/sim.9090 · Cited by 80
Eisen, E. A., Bardin, J. A., Gore, R. J., Woskie, S., Hallock, M. F., & Monson, R. R. (2001) · Scandinavian Journal of Work Environment & Health · doi:10.5271/sjweh.611 · Cited by 76
Bayesian decision analysis: principles and practice
Smith, J. Q. (2011) · Choice Reviews Online · doi:10.5860/choice.48-6339 · Cited by 76
A causal roadmap for generating high-quality real-world evidence
Dang, L. E., Gruber, S., Lee, H., Dahabreh, I. J., Stuart, E. A., Williamson, B. D., Wyss, R., Diaz, I. M., Ghosh, D., Kıcıman, E., et al. (2023) · Journal of Clinical and Translational Science · doi:10.1017/cts.2023.635 · Cited by 85
Shepherd, B. E., Gilbert, P. B., Jemiai, Y., & Rotnitzky, A. G. (2005) · Biometrics · doi:10.1111/j.1541-0420.2005.00495.x · Cited by 74
Nested Markov properties for acyclic directed mixed graphs
Richardson, T. S., Evans, R. J., Robins, J. M., & Shpitser, I. (2023) · The Annals of Statistics · doi:10.1214/22-aos2253 · Cited by 95
Self-citationGain Scores Revisited: A Graphical Models Perspective
Kim, Y., & Steiner, P. M. (2019) · Sociological Methods & Research · doi:10.1177/0049124119826155 · Cited by 74
History-Adjusted Marginal Structural Models and Statically-Optimal Dynamic Treatment Regimens
van der Laan, M. J., Petersen, M. L., & Joffe, M. M. (2005) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1003 · Cited by 73
Dependent Happenings: a Recent Methodological Review
Halloran, M. E., & Hudgens, M. G. (2016) · Current Epidemiology Reports · doi:10.1007/s40471-016-0086-4 · Cited by 73
Causal Inference and Estimands in Clinical Trials
Lipkovich, I. A., Ratitch, B., & Mallinckrodt, C. H. (2019) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2019.1697739 · Cited by 75
Liu, F., Xu, J., Guo, L., Qin, W., Liang, M., Schumann, G., & Yu, C. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01669-6 · Cited by 72
Lin, S., Young, J. G., Logan, R. W., Tchetgen, E. J. T., & VanderWeele, T. J. (2016) · Epidemiology · doi:10.1097/ede.0000000000000609 · Cited by 77
COVID-19 Outcomes Among Solid Organ Transplant Recipients: A Case-control Study
Sharma, P., Chen, V. L., Fung, C., Troost, J. P., Patel, V. N., Combs, M. P., Norman, S. P., Garg, P., Colvin, M. M., Aaronson, K. D., et al. (2020) · Transplantation · doi:10.1097/tp.0000000000003447 · Cited by 72
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Cui, Y., Pu, H., Shi, X., Miao, W., & Tchetgen, E. T. (2023) · Journal of the American Statistical Association · doi:10.1080/01621459.2023.2191817 · Cited by 113
Chevrier, J., Picciotto, S., & Eisen, E. A. (2012) · Epidemiology · doi:10.1097/ede.0b013e318245fc06 · Cited by 71
Edwards, J. K., Cole, S. R., & Westreich, D. J. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyu272 · Cited by 71
Oksanen, T., Mika Kivimaki, B., Kawachi, I. I., Subramanian, S. V., Takao, S., Suzuki, E., Kouvonen, A. M., Pentti, J., Salo, P., Virtanen, M., et al. (2011) · American Journal of Public Health · doi:10.2105/ajph.2011.300166 · Cited by 71
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Robins, J. M. (2014) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat06059 · Cited by 71
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Bulka, C. M., Avula, V. R., & Fry, R. C. (2021) · Environmental Pollution · doi:10.1016/j.envpol.2021.116619 · Cited by 70
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Sun, T. F., Wang, W., Jing, L., Cui, Y., Song, X., & Nie, L. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548211 · Cited by 70
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Self-citationLundberg, I. (2022) · Sociological Methods & Research · doi:10.1177/00491241211055769 · Cited by 72
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Finkelstein, D. M., Goggins, W. B., & Schoenfeld, D. (2002) · Biometrics · doi:10.1111/j.0006-341x.2002.00298.x · Cited by 69
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Self-citationShortreed, S. M., Cook, A. J., Coley, R. Y., Bobb, J. F., & Nelson, J. C. (2019) · American Journal of Epidemiology · doi:10.1093/aje/kwy292 · Cited by 74
Lodi, S., Phillips, A. N., Logan, R. W., Olson, A. D., Costagliola, D. G., Abgrall, S., van Sighem, A., Reiss, P., Miró, J. M., Ferrer, E., et al. (2015) · The Lancet HIV · doi:10.1016/s2352-3018(15)00108-3 · Cited by 68
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Keiding, N., & Clayton, D. G. (2014) · Statistical Science · doi:10.1214/13-sts453 · Cited by 68
Ding, W., & Lehrer, S. (2010) · The Review of Economics and Statistics · doi:10.1162/rest.2009.11453 · Cited by 68
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Humphreys, P. W., & Freedman, D. A. (1996) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/47.1.113 · Cited by 68
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“…are no treatment spillovers across units (Cox (1958)).3 3The idea of defining potential outcomes as a function of assignment paths first appears in Robins (1986) and has been further developed in subsequent work such as Robins (1994), Robins, Greenland, and Hu (1999), Murphy et al. (2001),…”
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Metadata
- Authors
- Robins, J.
- Affiliations
- Harvard University (US)
- Corresponding author
- Robins, James
- ORCID
- Robins, James
- Venue
- Mathematical Modelling
- Publisher
- Elsevier BV
- Published
- 1986
- Volume
- 7
- Issue
- 9-12
- Pages
- 1393-1512
- ISSN
- 0270-0255 (ISSN-L)
- OpenAlex
- W2082299845
- Cited by
- 2,451
- References
- 20
- Citations by year
- 2026: 148 · 2025: 177 · 2024: 221 · 2023: 167 · 2022: 182 · 2021: 167 · 2020: 170 · 2019: 137 · 2018: 107 · 2017: 104 · 2016: 99 · 2015: 98 · 2014: 100 · 2013: 67 · 2012: 85
- FWCI
- 4.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Mathematical Modelling (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- causal inference, mortality studies, healthy worker survivor effect, occupational mortality, arsenic exposure, copper smelter workers, lung cancer mortality, all-cause mortality, causal graphical models, mortality risk factors
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