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1986年，因本斯在赫尔大学的导师之一--安东尼·兰开斯特搬到了位于罗德岛普罗维登斯的布朗大学。因本斯跟随兰开斯特到布朗大学继续攻读研究生和博士学位，并在1989年和1991年在布朗大学获得了经济学硕士和博士学位。
1986年，因本斯在赫尔大学的导师之一--安东尼·兰开斯特搬到了位于罗德岛普罗维登斯的布朗大学。因本斯跟随兰开斯特到布朗大学继续攻读研究生和博士学位，并于1989年和1991年在布朗大学获得了经济学硕士和博士学位。
==Career==
==Career==
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事业生涯
因本斯曾分别任教于哈佛大学（1990-1997，2006-2012年），蒂尔堡大学（1989-1990年），加州大学洛杉矶分校（1997-2001年），加州大学伯克利分校（2002-06年）。他主要研究计量经济学，这是一种描绘因果推理的特殊方法。他于2019年成为《Econometrica》杂志的编辑，并将担任这一职务直到2023年。截至2021年，他是斯坦福大学商学院应用计量经济学和经济学教授。他还是斯坦福大学经济政策研究所（SIEPR）的高级研究员，以及该所人文与社会科学学院的经济学教授。因本斯是经济计量学会（2001年）和美国艺术与科学学院的研究员。2017年，因本斯被选为荷兰皇家艺术与科学学院的外籍成员。2020年，他被评选为美国统计协会会员。
=== Econometrics and work on causal relationships ===
=== Econometrics and work on causal relationships ===
计量经济学和因果关系研究
计量经济学和因果关系研究
与经济学家同行，包括约书亚·安格里斯特和艾伦·克鲁格合作，因本斯致力于开发方法论和框架，帮助经济学家利用现实生活中的情况（即自然试验）来检验现实生活中的理论。通过他的研究，帮助经济学家分析因果关系，通过他研究分析的一些问题，包括大学教育或额外的教育年限对收入的影响，使得因果关系研究框架在很多其他领域也有了应用，包括社会学和生物医学。他的工作为跨学科的研究人员提供了理解现实世界工具，提高了他们更好地理解基于实地和试验数据的干预措施的效果的能力。这些方法有助于研究人员分析各种研究问题，包括研究新政策对经济活动和新药对患者有效性的影响等。
In one of his earliest collaborations with Angrist, Imbens introduced a model called [[Local average treatment effect|Local Average Treatment Effect (LATE)]] that helped researches to draw [[causal inference]] from observational data. Elaborating on the model in a ''[[Econometrica]]'' paper in 1994 titled "Identification and Estimation of Local Average Treatment Effects", the pair employed the idea of [[natural experiment]]s, which were real world events and situations as against controlled conditions to study the effects of key changes. In doing so, the pair took advantage of the role chance and randomization that naturally occurred in the real world rather than controlled simulations, which could be expensive, time-consuming, or even unethical.<ref>{{Cite book|last=D.|first=Angrist, Joshua|url=http://worldcat.org/oclc/1144555780|title=Identification and Estimation of Local Average Treatment Effects|oclc=1144555780}}</ref><ref name=":2" /> The paper and the model had significant impact on other research efforts across econometrics, statistics and other fields.<ref name=":2" />
In one of his earliest collaborations with Angrist, Imbens introduced a model called [[Local average treatment effect|Local Average Treatment Effect (LATE)]] that helped researches to draw [[causal inference]] from observational data. Elaborating on the model in a ''[[Econometrica]]'' paper in 1994 titled "Identification and Estimation of Local Average Treatment Effects", the pair employed the idea of [[natural experiment]]s, which were real world events and situations as against controlled conditions to study the effects of key changes. In doing so, the pair took advantage of the role chance and randomization that naturally occurred in the real world rather than controlled simulations, which could be expensive, time-consuming, or even unethical.<ref>{{Cite book|last=D.|first=Angrist, Joshua|url=http://worldcat.org/oclc/1144555780|title=Identification and Estimation of Local Average Treatment Effects|oclc=1144555780}}</ref><ref name=":2" /> The paper and the model had significant impact on other research efforts across econometrics, statistics and other fields.<ref name=":2" />
'''的一些工作被总结在一本书中，这本书是与美国统计学家 Donald b. Rubin 合著的，书名是《统计、社会和生医科学的因果推断》。'''
'''的一些工作被总结在一本书中，这本书是与美国统计学家 Donald b. Rubin 合著的，书名是《统计、社会和生医科学的因果推断》。'''
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因本斯最近的一些工作被总结在与美国统计学家唐纳德·B.鲁宾的合著作品中，书名是《统计、社会和生医科学的因果推断》。
More recently, he (along with Prof. Susan Athey) has been working on using machine learning methods, particularly modifications to random forests called causal forests<ref>{{cite web |title=Causal Tree R package; Authors -- Susan Athey, Guido Imbens, Yangyang Kong & Vikas Ramachandra |url=https://github.com/susanathey/causalTree/blob/master/doc/briefintro.pdf}}</ref><ref>{{cite web |title=Recursive partitioning for heterogeneous causal effects; Authors -- Susan Athey and Guido Imbens |url=https://www.pnas.org/content/113/27/7353.short |access-date=13 October 2021 |archive-date=29 July 2021 |archive-url=https://web.archive.org/web/20210729101951/https://www.pnas.org/content/113/27/7353.short |url-status=live }}</ref> to estimate heterogeneous treatment effects in causal inference models.
More recently, he (along with Prof. Susan Athey) has been working on using machine learning methods, particularly modifications to random forests called causal forests<ref>{{cite web |title=Causal Tree R package; Authors -- Susan Athey, Guido Imbens, Yangyang Kong & Vikas Ramachandra |url=https://github.com/susanathey/causalTree/blob/master/doc/briefintro.pdf}}</ref><ref>{{cite web |title=Recursive partitioning for heterogeneous causal effects; Authors -- Susan Athey and Guido Imbens |url=https://www.pnas.org/content/113/27/7353.short |access-date=13 October 2021 |archive-date=29 July 2021 |archive-url=https://web.archive.org/web/20210729101951/https://www.pnas.org/content/113/27/7353.short |url-status=live }}</ref> to estimate heterogeneous treatment effects in causal inference models.
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此外，他与Susan Athey教授一直在研究使用机器学习方法，特别是对随机森林进行修正，以估计因果推理模型中的异质处理效应。
===Nobel Memorial Prize in Economics===
===Nobel Memorial Prize in Economics===
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2002年，因本斯与经济学家苏珊·埃塞结婚。她是一位同样在斯坦福大学商学院教书的经济学家。在Imbens 和埃塞的婚礼上，伴郎是约书亚·安格瑞斯特(Joshua Angrist) ，19年后，因本斯与他分享了诺贝尔奖。
He holds [[Multiple citizenship|dual citizenship]] in the United States and Netherlands.<ref name="Imbens_Curriculum_Vitae" />
He holds [[Multiple citizenship|dual citizenship]] in the United States and Netherlands.<ref name="Imbens_Curriculum_Vitae" />