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{{distinguish|James Robbins (disambiguation){{!}}James Robbins}}

{{distinguish|James Robbins (disambiguation){{!}}James Robbins}}

{{Infobox scientist

{{Infobox scientist

| name              = James M. Robins

| name              = 詹姆斯·M·罗宾斯

| image            = James Robins.jpg <!--(filename only, i.e. without "File:" prefix)-->

| image            = James Robins.jpg <!--(filename only, i.e. without "File:" prefix)-->

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| image_size        = 220px

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| caption          = Robins at the [[Mathematical Research Institute of Oberwolfach]] in 2012

| caption          = 2012年罗宾斯在 [[德国奥伯沃尔夫数学研究所]]

| birth_date        = <!--{{birth date |YYYY|MM|DD}}-->

| birth_date        = <!--{{birth date |YYYY|MM|DD}}-->

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| death_date        = <!--{{death date and age |YYYY|MM|DD |YYYY|MM|DD}} (death date then birth date)-->

| death_date        = <!--{{death date and age |YYYY|MM|DD |YYYY|MM|DD}} (death date then birth date)-->

| death_place      =  

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| nationality      = [[United States|American]]

| nationality      = [[美国]]

| fields            = [[Epidemiology]]

| fields            = [[流行病学]]

| workplaces        = [[Harvard School of Public Health]]

| workplaces        = [[哈佛大学公共卫生学院]]

| alma_mater        = [[Washington University in St. Louis]]<br>[[Harvard University]]

| alma_mater        = [[圣路易斯华盛顿大学]]<br>[[哈佛大学]]

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| awards            = [[Nathan Mantel]] Award (2013)

| awards            = [[内森·曼特尔]] 奖 (2013)

}}

}}

【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。

【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 journal]]s and is an [[ISI highly cited researcher]].<ref>[http://hcr3.isiknowledge.com/author.cgi?id=5977 Robins, James] at [[ISIHighlyCited.com]]</ref>

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 journal]]s and is an [[ISI highly cited researcher]].<ref name=":0">[http://hcr3.isiknowledge.com/author.cgi?id=5977 Robins, James] at [[ISIHighlyCited.com]]</ref>

== Biography ==

== 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,<ref>Thomas S. Richardson and Andrea Rotnitzky, [https://projecteuclid.org/journals/statistical-science/volume-29/issue-4/Causal-Etiology-of-the-Research-of-James-M-Robins/10.1214/14-STS505.full Causal Etiology of the Research of James M. Robins], Statist. Sci. 29 (4) 459-484, 2014. [https://doi.org/10.1214/14-STS505 doi:10.1214/14-STS505]</ref> 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.

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,<ref name=":1">Thomas S. Richardson and Andrea Rotnitzky, [https://projecteuclid.org/journals/statistical-science/volume-29/issue-4/Causal-Etiology-of-the-Research-of-James-M-Robins/10.1214/14-STS505.full Causal Etiology of the Research of James M. Robins], Statist. Sci. 29 (4) 459-484, 2014. [https://doi.org/10.1214/14-STS505 doi:10.1214/14-STS505]</ref> 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.

== Research ==

== 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.<ref>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>

【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。

【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" />。

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>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>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]].

【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。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。

【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。

== Selected publications ==

== Selected publications ==