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Do we need flexible machine-learning algorithms to assess the effect of long-term exposure to fine particulate matter on mortality?: An example from a Canadian national cohort.我们是否需要灵活的机器学习算法来评估长期暴露于细颗粒物对死亡率的影响？：来自加拿大全国队列的一个例子。
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The Causal Roadmap and Simulations to Improve the Rigor and Reproducibility of Real-data Applications.改善真实数据应用的严谨性和可重复性的因果关系图和模拟。
Epidemiology. 2024 Nov 1;35(6):791-800. doi: 10.1097/EDE.0000000000001773. Epub 2024 Aug 1.
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Statistical plasmode simulations-Potentials, challenges and recommendations.统计等离子体模拟——潜力、挑战和建议。
Stat Med. 2024 Apr 30;43(9):1804-1825. doi: 10.1002/sim.10012. Epub 2024 Feb 14.
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Assessing the causal effect of policies: an example using stochastic interventions.评估政策的因果效应：一个使用随机干预的例子。
Int J Biostat. 2013 Nov 19;9(2):161-74. doi: 10.1515/ijb-2013-0014.
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Estimation of the effect of interventions that modify the received treatment.评估改变所接受治疗的干预措施的效果。
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Comment on "Do we need flexible machine-learning algorithms to assess the effect of long-term exposure to fine particulate matter on mortality?".

作者信息

Williams Nicholas, Rudolph Kara E, Díaz Iván

机构信息

Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York.

Division of Biostatistics, Grossman School of Medicine, New York University, New York, New York.

出版信息

Environ Epidemiol. 2025 Jul 14;9(4):e407. doi: 10.1097/EE9.0000000000000407. eCollection 2025 Aug.

DOI:10.1097/EE9.0000000000000407
PMID:40666261
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12262988/
Abstract
摘要