Causal Machine Learning Analysis of All-Cause Mortality in Japanese Atomic-Bomb Survivors Article Swipe
Igor Shuryak
,
Zhenqiu Liu
,
Eric Wang
,
Xiao Yu Wu
,
Robert L. Ullrich
,
Alina V. Brenner
,
Munechika Misumi
,
David J. Brenner
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.09.03.25335009
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.09.03.25335009
The health consequences of ionizing radiation have long been studied, yet significant uncertainties remain, particularly at low doses. In particular, traditional dose-response models such as linear, linear-quadratic, threshold, or hormesis models, all impose specific assumptions about low-dose effects. In addition, while the goal of radiation epidemiological studies is ideally to uncover causal relationships between dose and health effects, most conventional data analysis techniques can only establish associations rather than causation. These limitations highlight the need for new analysis methodologies that can eliminate the need for a priori dose-response assumptions and can provide causal inferences more directly based on observational data. Causal Machine Learning (CML) is a new approach designed to uncover how changes in one variable directly influence another, and with these motivations, a CML approach was, for the first time, implemented here to analyze radiation epidemiological data – in this case all-cause mortality data from Japanese A-bomb survivors. Compared to more traditional parametric approaches for analyzing radiation epidemiological data such as Poisson regression, CML makes no a priori assumptions about dose-effect response shapes ( e.g., linearity or thresholds). Extensive validation and refutation tests indicated that the proposed CML methodology is robust and is not overly sensitive to unmeasured confounding and noise. At moderate to high radiation doses, the CML analysis supports a causal increase in mortality with radiation exposure, with a statistically significant positive average treatment effect (p = 0.014). By contrast, no statistically significant causal increase in all-cause mortality was detected at doses below 0.05 Gy (50 mGy). These conclusions were drawn after adjusting for all available key covariates including attained age, age at exposure, and sex. We emphasize that this CML-based approach is not designed to validate or disprove any particular dose-response model. Rather this approach represents a new potentially complementary approach that does not rely on a priori functional form assumptions.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.09.03.25335009
- https://www.medrxiv.org/content/medrxiv/early/2025/09/05/2025.09.03.25335009.full.pdf
- OA Status
- green
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4414006301
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4414006301Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.09.03.25335009Digital Object Identifier
- Title
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Causal Machine Learning Analysis of All-Cause Mortality in Japanese Atomic-Bomb SurvivorsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-09-05Full publication date if available
- Authors
-
Igor Shuryak, Zhenqiu Liu, Eric Wang, Xiao Yu Wu, Robert L. Ullrich, Alina V. Brenner, Munechika Misumi, David J. BrennerList of authors in order
- Landing page
-
https://doi.org/10.1101/2025.09.03.25335009Publisher landing page
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https://www.medrxiv.org/content/medrxiv/early/2025/09/05/2025.09.03.25335009.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.medrxiv.org/content/medrxiv/early/2025/09/05/2025.09.03.25335009.full.pdfDirect OA link when available
- Concepts
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Nuclear weapon, Psychology, Nuclear physics, PhysicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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42Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3043000552, https://openalex.org/W2158701811, https://openalex.org/W4323539333, https://openalex.org/W3101136638, https://openalex.org/W2770648435, https://openalex.org/W2171465583, https://openalex.org/W4385987204, https://openalex.org/W2052615338, https://openalex.org/W3043097434, https://openalex.org/W4387570902, https://openalex.org/W2041405779, https://openalex.org/W1985425765, https://openalex.org/W4384283546, https://openalex.org/W1966648402, https://openalex.org/W2068530472, https://openalex.org/W4317659293, https://openalex.org/W4389421161, https://openalex.org/W2883283279, https://openalex.org/W4214601498, https://openalex.org/W2599903518, https://openalex.org/W2154438710, https://openalex.org/W4385852698, https://openalex.org/W2918651516, https://openalex.org/W2917286209, https://openalex.org/W3123436326, https://openalex.org/W4301430412, https://openalex.org/W2208550830, https://openalex.org/W4288570810, https://openalex.org/W4391389316, https://openalex.org/W4394964969, https://openalex.org/W4391591690, https://openalex.org/W2022450888, https://openalex.org/W1516659296, https://openalex.org/W4281723409, https://openalex.org/W2911964244, https://openalex.org/W2295598076, https://openalex.org/W2122825543, https://openalex.org/W4383959968, https://openalex.org/W2146774335, https://openalex.org/W2962862931, https://openalex.org/W4287558430, https://openalex.org/W2903269435 |
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