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

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

【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<ref name=":0" />。

【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<ref name=":0" />。

== Biography ==

== 个人自传 ==

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.

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.

【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<ref name=":1" />，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。

【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<ref name=":1" />，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 ==

== 研究领域 ==

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>

【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。

【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 ==

== 精选发表论文 ==

* {{cite journal

* {{cite journal

  | author = Robins, J.M.

  | author = Robins, J.M.

*  

*  

== Notes ==

== 参考文献 ==

{{Reflist}}

{{Reflist}}

== References ==

== 外部链接 ==

* [http://www.hsph.harvard.edu/faculty/james-robins/ James Robins — Mitchell L. and Robin LaFoley Dong Professor of Epidemiology]. Harvard School of Public Health (Accessed 15 March 2008).

* [http://www.hsph.harvard.edu/faculty/james-robins/ James Robins — Mitchell L. and Robin LaFoley Dong Professor of Epidemiology]. Harvard School of Public Health (Accessed 15 March 2008).

* [http://www.hsph.harvard.edu/~robins/ Dr. James M. Robins — Bibliography] Harvard School of Public Health (Accessed 15 March 2008).

* [http://www.hsph.harvard.edu/~robins/ Dr. James M. Robins — Bibliography] Harvard School of Public Health (Accessed 15 March 2008).