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 treatment in each period is random conditional on the path of covariates and realized outcomes, rather than parallel trends.
“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)
The truncated factorization formula is not restricted to interventions on a single variable; it applies to simultaneous or sequential interventions, such as time-varying treatments with time-varying confounders.
“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
The truncated factorization formula is not restricted to interventions on a single variable; it is applicable to simultaneous or sequential interventions.
“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
The algebraic machinery of counterfactual notation can be powerful in refining assumptions, deriving consistent estimands, bounding probabilities of necessary and sufficient causation, and combining experimental and nonexperimental data.
“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”
From An Introduction to Causal Inference (2010) · cites this paper as Robins, 1986
Hypothetical entities Yx are assumed to be connected to observed variables via consistency constraints such as X=x implies Yx=Y.
“They are assumed to be connected to observed variables via consistency constraints (Robins, 1986) such as X = x =⇒ Y x = Y,(32)”
From An Introduction to Causal Inference (2010) · cites this paper as Robins, 1986
The 'Definition of a treatment' section discusses hypothetical survival histories under treatment, and Section 2C.3 relates planned and observed exposure histories. Neither passage states that a hypothetical outcome equals the observed outcome when the hypothetical treatment matches the treatment received. Searches for consistency and that outcome-equality condition did not find it in our copy. (LLM, conf=0.68)
Experimental — accuracy not yet measured
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.
“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]
The paper’s discussion of occupational exposure and mortality (Remark; §2C.3) does not address vaccination coverage or prioritizing people at the intersection of SARS-CoV-2 exposure, severity, and transmission risks. Searches for equitable vaccination coverage and vaccine prioritization found no passage addressing that claim. (LLM, conf=0.78)
Experimental — accuracy not yet measured
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Lechner, M., Miquel, R., & Wunsch, C. (2011) · Journal of the European Economic Association · doi:10.1111/j.1542-4774.2011.01029.x · Cited by 362 · see citing works
Emsley, R., Dunn, G., & White, I. R. (2010) · Statistical Methods in Medical Research · doi:10.1177/0962280209105014 · Cited by 356 · see citing works
Bias in occupational epidemiology studies
Pearce, N. E., Checkoway, H., & Kriebel, D. (2006) · Occupational and Environmental Medicine · doi:10.1136/oem.2006.026690 · Cited by 350 · see citing works
Healthy worker effect phenomenon: Revisited with emphasis on statistical methods – A review
Chowdhury, R., Shah, D., & Payal, A. R. (2017) · Indian Journal of Occupational and Environmental Medicine · doi:10.4103/ijoem.ijoem_53_16 · Cited by 347 · see citing works
Causal Inference without Counterfactuals
Dawid, A. P. (2001) · Applied logic series · doi:10.1007/978-94-017-1586-7_3 · Cited by 343 · see citing works
Graphs, Causality, and Structural Equation Models
PEARL, J. (1998) · Sociological Methods & Research · doi:10.1177/0049124198027002004 · Cited by 341 · see citing works
Intervening on risk factors for coronary heart disease: an application of the parametric g-formula.
Taubman, S., Robins, J., Mittleman, M., & Hernán, M. (2009) · International Journal of Epidemiology · doi:10.1093/ije/dyp192 · Cited by 359 · see citing works
Self-citationHernán, M. Á., Brumback, B., & Robins, J. M. (2002) · Statistics in Medicine · doi:10.1002/sim.1144 · Cited by 335 · see citing works
Self-citationYoung, J. G., Stensrud, M. J., Tchetgen, E. J. T., & Hernán, M. Á. (2020) · Statistics in Medicine · doi:10.1002/sim.8471 · Cited by 335 · see citing works
Estimation of the causal effects of time-varying exposures
Robins, J. M., & Hernán, M. Á. (2008) · Chapman & Hall/CRC interdisciplinary statistics series/Interdisciplinary statistics · doi:10.1201/9781420011579.ch23 · Cited by 330 · see citing works
Self-citationRobins, J. M., Blevins, D., Ritter, G. A., & Wulfsohn, M. S. (1992) · Epidemiology · doi:10.1097/00001648-199207000-00007 · Cited by 328 · see citing works
Self-citationStatistical methods for Mendelian randomization in genome-wide association studies: A review
Boehm, F. J., & Zhou, X. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.05.015 · Cited by 328 · see citing works
Elwert, F. (2013) · Handbooks of Sociology and Social Research · doi:10.1007/978-94-007-6094-3_13 · Cited by 325 · see citing works
Causal inference and longitudinal data: a case study of religion and mental health
VanderWeele, T. J., Jackson, J. W., & Li, S. (2016) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-016-1281-9 · Cited by 322 · see citing works
Causal Mediation Analysis with Multiple Mediators
Daniel, R., Stavola, B. L. D., Cousens, S. N., & Vansteelandt, S. (2014) · Biometrics · doi:10.1111/biom.12248 · Cited by 319 · see citing works
Robins, J. (1987) · Journal of Chronic Diseases · doi:10.1016/s0021-9681(87)80018-8 · Cited by 305 · see citing works
Self-citationAdvancing functional connectivity research from association to causation
Reid, A., Headley, D. B., Mill, R. D., Sánchez-Romero, R., Uddin, L. Q., Marinazzo, D., Lurie, D. J., Valdes-Sosa, P. A., Hanson, S. J., Biswal, B. B., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0510-4 · Cited by 303 · see citing works
Experimental design and primary data analysis methods for comparing adaptive interventions.
Nahum-Shani, I., Qian, M., Almirall, D., Pelham, W. E., Gnagy, B., Fabiano, G. A., Waxmonsky, J. G., Yu, J., & Murphy, S. A. (2012) · Psychological Methods · doi:10.1037/a0029372 · Cited by 298 · see citing works
Methods of Public Health Research — Strengthening Causal Inference from Observational Data
Hernán, M. Á. (2021) · New England Journal of Medicine · doi:10.1056/nejmp2113319 · Cited by 298 · see citing works
- 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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