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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”
From An Introduction to Causal Inference (2010) · cites this paper as Robins, 1986
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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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Weight Gain After Smoking Cessation and Lifestyle Strategies to Reduce it
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Self-citationVisual impairment and mortality in patients with type 2 diabetes
Siersma, V., Køster‐Rasmussen, R., Bruun, C., Olivarius, N. D. F., & Brunes, A. (2019) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2018-000638 · Cited by 23
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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