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− | 此词条暂由彩云小译翻译,翻译字数共647,未经人工整理和审校,带来阅读不便,请见谅。
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| {{distinguish|James Robbins (disambiguation){{!}}James Robbins}} | | {{distinguish|James Robbins (disambiguation){{!}}James Robbins}} |
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| 【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" />。 |
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− | == 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. |
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| 【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。 |
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− | == 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> |
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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。 | | 【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。 |
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− | == Selected publications == | + | == 精选发表论文 == |
| * {{cite journal | | * {{cite journal |
| | author = Robins, J.M. | | | author = Robins, J.M. |
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− | == Notes == | + | == 参考文献 == |
| {{Reflist}} | | {{Reflist}} |
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− | == 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). |