“James Robins”的版本间的差异

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In 1986, Robins published the paper "A New Approach to Causal Inference in Mortality Studies", which introduced a new framework for drawing causal inference from observational data. In this paper and in other articles published around the same time, Robins showed that in non-experimental data, exposure is almost always time-dependent, and that standard methods such as regression are therefore almost always biased. This framework is mathematically very closely related to [[Judea Pearl]]'s graphical framework Non-Parametric Structural Equations Models, which Pearl developed independently shortly thereafter. Pearl's graphical models are a more restricted version of this theory.<ref name=":2">Single World Intervention Graphs (SWIGs): A Unification of the Counterfactual and Graphical Approaches to Causality https://csss.uw.edu/files/working-papers/2013/wp128.pdf</ref>
 
In 1986, Robins published the paper "A New Approach to Causal Inference in Mortality Studies", which introduced a new framework for drawing causal inference from observational data. In this paper and in other articles published around the same time, Robins showed that in non-experimental data, exposure is almost always time-dependent, and that standard methods such as regression are therefore almost always biased. This framework is mathematically very closely related to [[Judea Pearl]]'s graphical framework Non-Parametric Structural Equations Models, which Pearl developed independently shortly thereafter. Pearl's graphical models are a more restricted version of this theory.<ref name=":2">Single World Intervention Graphs (SWIGs): A Unification of the Counterfactual and Graphical Approaches to Causality https://csss.uw.edu/files/working-papers/2013/wp128.pdf</ref>
  
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【终译】1986年，罗宾斯发表了《死亡率研究中因果推断的新方法》一文，该文介绍了一个从观测数据中进行因果推断的新框架。在这篇论文以及同时期发表的其他文章中，Robins 指出，在非实验数据中，暴露几乎总是与时间有关，因此回归等标准方法几乎总是带有偏差。这个框架在数学上和朱迪亚·珀尔不久之后自主研发的图形框架非参数结构方程模型是非常密切相关的。不过珀尔的图模型是这个理论的一个更加受限的版本<ref name=":2" />。
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【终译】1986年，罗宾斯发表了《死亡率研究中因果推断的新方法》一文，该文介绍了一个从观测数据中进行因果推断的新框架。在这篇论文以及同时期发表的其他文章中，罗宾斯指出，在非实验数据中，暴露几乎总是与时间有关，因此回归等标准方法几乎总是带有偏差。这个框架在数学上和朱迪亚·珀尔不久之后自主研发的图形框架非参数结构方程模型是非常密切相关的。不过珀尔的图模型是这个理论的一个更加受限的版本<ref name=":2" />。
  
 
In his original paper on causal inference, Robins described two new methods for controlling for confounding bias, which can be applied in the generalized setting of time-dependent exposures: The G-formula and G-Estimation of Structural Nested Models.  Later, he introduced a third class of models, [[Marginal structural model|Marginal Structural Models]], in which the parameters are estimated using inverse probability of treatment weights.  He has also contributed significantly to the theory of dynamic treatment regimes, which are of high significance in [[comparative effectiveness research]] and personalized medicine. Together with Andrea Rotnitzky and other colleagues, in 1994 he introduced doubly robust estimators (derived from the influence functions) for statistical parameters in causal inference and missing data problems. The theory for doubly robust estimators has been highly influential in the field of [causal inference] and has influenced practice in computer science, biostatistics, epidemiology, machine learning, social sciences, and statistics.<ref name=":3">Michele Jonsson Funk, Daniel Westreich, Chris Wiesen, Til Stürmer, M. Alan Brookhart, Marie Davidian, Doubly Robust Estimation of Causal Effects, American Journal of Epidemiology, Volume 173, Issue 7, 1 April 2011, Pages 761–767, https://doi.org/10.1093/aje/kwq439</ref><ref name=":4">https://towardsdatascience.com/double-machine-learning-for-causal-inference-78e0c6111f9d Retrieved 28 November 2021.</ref>  In 2008, he also developed the theory of higher-order influence functions for statistical functional estimation with collaborators including Lingling Li, Eric Tchetgen Tchetgen, and [[Aad van der Vaart]].
 
In his original paper on causal inference, Robins described two new methods for controlling for confounding bias, which can be applied in the generalized setting of time-dependent exposures: The G-formula and G-Estimation of Structural Nested Models.  Later, he introduced a third class of models, [[Marginal structural model|Marginal Structural Models]], in which the parameters are estimated using inverse probability of treatment weights.  He has also contributed significantly to the theory of dynamic treatment regimes, which are of high significance in [[comparative effectiveness research]] and personalized medicine. Together with Andrea Rotnitzky and other colleagues, in 1994 he introduced doubly robust estimators (derived from the influence functions) for statistical parameters in causal inference and missing data problems. The theory for doubly robust estimators has been highly influential in the field of [causal inference] and has influenced practice in computer science, biostatistics, epidemiology, machine learning, social sciences, and statistics.<ref name=":3">Michele Jonsson Funk, Daniel Westreich, Chris Wiesen, Til Stürmer, M. Alan Brookhart, Marie Davidian, Doubly Robust Estimation of Causal Effects, American Journal of Epidemiology, Volume 173, Issue 7, 1 April 2011, Pages 761–767, https://doi.org/10.1093/aje/kwq439</ref><ref name=":4">https://towardsdatascience.com/double-machine-learning-for-causal-inference-78e0c6111f9d Retrieved 28 November 2021.</ref>  In 2008, he also developed the theory of higher-order influence functions for statistical functional estimation with collaborators including Lingling Li, Eric Tchetgen Tchetgen, and [[Aad van der Vaart]].
  
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【终译】在其关于因果推断的原始论文中，Robins 描述了两种新的控制混杂偏差的方法，这两种方法可以应用于与时间相关暴露的广义设定: 结构嵌套模型的 G公式和 G 估计。后来，他介绍了第三类模型，边际结构模型，其中的参数估计使用逆概率处理权重。他还对动态治疗机制的理论做出了重要贡献，这在比较效益研究和个体化医学中都具有重要意义。1994年，他与安德里亚 · 罗特尼茨基及其他同事一起，为因果推断和缺失数据问题中的统计参数引入了双重稳健估计(由影响函数导出)。双重稳健估计理论在因果推断领域具有很大的影响力，并影响了计算机科学、生物统计学、流行病学、机器学习、社会科学和统计学的实践<ref name=":3" /><ref name=":4" />。2008年，他还与李，艾瑞克和阿德合作，发展了用于统计功能估计的高阶影响函数理论。
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【终译】在其关于因果推断的原始论文中，罗宾斯描述了两种新的控制混杂偏差的方法，这两种方法可以应用于与时间相关暴露的广义设定: 结构嵌套模型的 G公式和 G 估计。后来，他介绍了第三类模型，边际结构模型，其中的参数估计使用逆概率处理权重。他还对动态治疗机制的理论做出了重要贡献，这在比较效益研究和个体化医学中都具有重要意义。1994年，他与安德里亚 · 罗特尼茨基及其他同事一起，为因果推断和缺失数据问题中的统计参数引入了双重稳健估计(由影响函数导出)。双重稳健估计理论在因果推断领域具有很大的影响力，并影响了计算机科学、生物统计学、流行病学、机器学习、社会科学和统计学的实践<ref name=":3" /><ref name=":4" />。2008年，他还与李，艾瑞克和阿德合作，发展了用于统计功能估计的高阶影响函数理论。
  
 
== Selected publications ==
 
== Selected publications ==

2022年5月9日 (一) 21:25的版本

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模板:Distinguish

詹姆斯·M·罗宾斯
220px
Nationality美国
Alma mater圣路易斯华盛顿大学
哈佛大学
Awards内森·曼特尔 奖 (2013)
Scientific career
Fields流行病学
Institutions哈佛大学公共卫生学院

James M. Robins is an epidemiologist and biostatistician best known for advancing methods for drawing causal inferences from complex observational studies and randomized trials, particularly those in which the treatment varies with time. He is the 2013 recipient of the Nathan Mantel Award for lifetime achievement in statistics and epidemiology.

【终译】詹姆斯 · M · 罗宾斯是一位流行病学家和生物统计学家，他最著名的研究方法是从复杂的观察研究和随机试验中提取因果推论，特别是那些治疗随时间变化的试验。他是2013年内森 · 曼特尔统计学和流行病学终身成就奖的获得者。

He graduated in medicine from Washington University in St. Louis in 1976. He is currently Mitchell L. and Robin LaFoley Dong Professor of Epidemiology at Harvard T.H. Chan School of Public Health. He has published over 100 papers in academic journals and is an ISI highly cited researcher.[1]

【终译】他于1976年在圣路易斯华盛顿大学毕业，获得医学学位。目前，他担任哈佛大学陈曾熙公共卫生学院传染病系的米切尔和罗宾 · 拉弗利东校级教授。他在学术期刊上发表了超过100篇论文，是科学信息研究所的高引用学者[1]。

Biography

Robins attended Harvard College with the class of 1971, concentrating in mathematics and philosophy. He was elected to Phi Beta Kappa in his junior year, but did not graduate. He went on to attend Washington University School of Medicine, graduating in 1976,[2] and practiced Occupational Medicine for several years. While working in occupational medicine, he attended basic courses in applied medical statistics at the Yale School of Public Health, but quickly came to the conclusion that the methodology used at the time was insufficiently rigorous to support causal conclusions.

【终译】罗宾斯于1971年进入哈佛大学，主修数学和哲学。在大三时被选为斐陶斐荣誉学会会员，但并未毕业，之后他进入华盛顿大学医学院，于1976年毕业[2]，并从事职业医学多年。在以职业医学领域从事期间，他参加了耶鲁大学公共卫生学院应用统计学的基础课程，但很快得出结论，当时使用的方法不够严谨，无法支撑因果结论。

Research

In 1986, Robins published the paper "A New Approach to Causal Inference in Mortality Studies", which introduced a new framework for drawing causal inference from observational data. In this paper and in other articles published around the same time, Robins showed that in non-experimental data, exposure is almost always time-dependent, and that standard methods such as regression are therefore almost always biased. This framework is mathematically very closely related to Judea Pearl's graphical framework Non-Parametric Structural Equations Models, which Pearl developed independently shortly thereafter. Pearl's graphical models are a more restricted version of this theory.[3]

【终译】1986年，罗宾斯发表了《死亡率研究中因果推断的新方法》一文，该文介绍了一个从观测数据中进行因果推断的新框架。在这篇论文以及同时期发表的其他文章中，罗宾斯指出，在非实验数据中，暴露几乎总是与时间有关，因此回归等标准方法几乎总是带有偏差。这个框架在数学上和朱迪亚·珀尔不久之后自主研发的图形框架非参数结构方程模型是非常密切相关的。不过珀尔的图模型是这个理论的一个更加受限的版本[3]。

In his original paper on causal inference, Robins described two new methods for controlling for confounding bias, which can be applied in the generalized setting of time-dependent exposures: The G-formula and G-Estimation of Structural Nested Models. Later, he introduced a third class of models, Marginal Structural Models, in which the parameters are estimated using inverse probability of treatment weights. He has also contributed significantly to the theory of dynamic treatment regimes, which are of high significance in comparative effectiveness research and personalized medicine. Together with Andrea Rotnitzky and other colleagues, in 1994 he introduced doubly robust estimators (derived from the influence functions) for statistical parameters in causal inference and missing data problems. The theory for doubly robust estimators has been highly influential in the field of [causal inference] and has influenced practice in computer science, biostatistics, epidemiology, machine learning, social sciences, and statistics.[4][5] In 2008, he also developed the theory of higher-order influence functions for statistical functional estimation with collaborators including Lingling Li, Eric Tchetgen Tchetgen, and Aad van der Vaart.

【终译】在其关于因果推断的原始论文中，罗宾斯描述了两种新的控制混杂偏差的方法，这两种方法可以应用于与时间相关暴露的广义设定: 结构嵌套模型的 G公式和 G 估计。后来，他介绍了第三类模型，边际结构模型，其中的参数估计使用逆概率处理权重。他还对动态治疗机制的理论做出了重要贡献，这在比较效益研究和个体化医学中都具有重要意义。1994年，他与安德里亚 · 罗特尼茨基及其他同事一起，为因果推断和缺失数据问题中的统计参数引入了双重稳健估计(由影响函数导出)。双重稳健估计理论在因果推断领域具有很大的影响力，并影响了计算机科学、生物统计学、流行病学、机器学习、社会科学和统计学的实践[4][5]。2008年，他还与李，艾瑞克和阿德合作，发展了用于统计功能估计的高阶影响函数理论。

Selected publications

  • Robins, J.M. (1989). "The control of confounding by intermediate variables". Statistics in Medicine. 8 (6): 679–701. doi:10.1002/sim.4780080608. PMID 2749074.
  • Robins, J.M.; Tsiatis, A.A. (1991). "Correcting for non-compliance in randomized trials using rank preserving structural failure time models". Communications in Statistics - Theory and Methods. 20 (8): 2609–2631. doi:10.1080/03610929108830654.
  • Robins, J.M. (1994). "Correcting for non-compliance in randomized trials using structural nested mean models". Communications in Statistics - Theory and Methods. 23 (8): 2379–2412. doi:10.1080/03610929408831393.
  • Robins, J.M. (1997). "Causal inference from complex longitudinal data". In M. Berkane. Latent Variable Modeling and Applications to Causality. Lecture Notes in Statistics. 120. Springer-Verlag. pp. 69–117. 
  • Robins, J.M.; Ritov, Y. (1997). "Toward A Curse Of Dimensionality Appropriate (CODA) Asymptotic Theory For Semi-parametric Models". Statistics in Medicine. 16 (3): 285–319. doi:10.1002/(SICI)1097-0258(19970215)16:3<285::AID-SIM535>3.3.CO;2-R. PMID 9004398.
  • Robins, J.M. (1998). "Correction for non-compliance in equivalence trials". Statistics in Medicine. 17 (3): 269–302. doi:10.1002/(SICI)1097-0258(19980215)17:3<269::AID-SIM763>3.0.CO;2-J. PMID 9493255.
  • Robins, J.M.; Hernan, M.A.; Brumback, B. (2000). "Marginal Structural Models and Causal Inference in Epidemiology". Epidemiology. 11 (5): 550–560. CiteSeerX 10.1.1.116.7039. doi:10.1097/00001648-200009000-00011. JSTOR 3703997. PMID 10955408. S2CID 8907527.
  • van der Laan, M.J.; Robins, J.M. (2003). Unified Methods for Censored Longitudinal Data and Causality. Springer Series in Statistics. Springer. ISBN 978-0-387-95556-8. 

Notes

  1. ↑ 1.0 1.1 Robins, James at ISIHighlyCited.com
  2. ↑ 2.0 2.1 Thomas S. Richardson and Andrea Rotnitzky, Causal Etiology of the Research of James M. Robins, Statist. Sci. 29 (4) 459-484, 2014. doi:10.1214/14-STS505
  3. ↑ 3.0 3.1 Single World Intervention Graphs (SWIGs): A Unification of the Counterfactual and Graphical Approaches to Causality https://csss.uw.edu/files/working-papers/2013/wp128.pdf
  4. ↑ 4.0 4.1 Michele Jonsson Funk, Daniel Westreich, Chris Wiesen, Til Stürmer, M. Alan Brookhart, Marie Davidian, Doubly Robust Estimation of Causal Effects, American Journal of Epidemiology, Volume 173, Issue 7, 1 April 2011, Pages 761–767, https://doi.org/10.1093/aje/kwq439
  5. ↑ 5.0 5.1 https://towardsdatascience.com/double-machine-learning-for-causal-inference-78e0c6111f9d Retrieved 28 November 2021.

References

  • 詹姆斯 · 罗宾斯ー米切尔和罗宾 · 拉弗利东流行病学教授。哈佛大学公共卫生学院(2008年3月15日访问)。
  • 詹姆斯 · 罗宾斯博士ー自传 哈佛大学公共卫生学院书目(2008年3月15日访问)。
  • 格尔曼，伊丽莎白(2006年3月23日) ：“詹姆斯 · 罗宾斯让统计数据说出真相: 数字服务于健康”哈佛大学公报。


Category:American epidemiologists Category:American statisticians Category:Year of birth missing (living people) Category:Harvard School of Public Health faculty Category:Living people Category:Washington University School of Medicine alumni Category:Biostatisticians Category:Fellows of the American Statistical Association Category:Harvard College alumni

分类: 美国流行病学家分类: 美国统计学家分类: 出生缺失年(活人)分类: 哈佛大学公共卫生学院教员分类: 活人分类: 华盛顿大学医学院校友分类: 生物统计学家分类: 美国统计协会会员分类: 哈佛大学校友


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