以赛亚-安德鲁 计量经济学家

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Isaiah Andrews
Econometrician | Class of 2020
Developing robust methods of statistical inference to address key challenges in economics and social science.


Portrait of Isaiah Andrews

Title
Econometrician
Affiliation
Department of Economics, Harvard University
Location
Cambridge, Massachusetts
Age
34 at time of award
Area of Focus
Economics
Website
Harvard University: Isaiah Andrews
Published October 6, 2020
ABOUT ISAIAH'S WORK
Isaiah Andrews is an econometrician developing reliable and broadly applicable methods of statistical inference to address key challenges in economics, social science, and medicine. Statistical inference is the process by which properties of a population are deduced based on a sample of data drawn from that population. Working across a variety of research areas and with a range of collaborators, Andrews incorporates ideas from differential geometry and decision theory to build technically rigorous statistical inference methods that overcome common problems encountered in applied empirical work.

Much of Andrews’s work has focused on problems of weak identification, where information is limited and the validity of many standard inference procedures is thus called into question. The causal effect of one variable on another is said to be identified if it could be learned from a sufficiently large dataset. Weak identification arises when a small change in the distribution of the data would eliminate our ability to determine the causal effect. In an examination of non-linear models, Andrews and a collaborator produced a pathbreaking analysis of the geometric structure of weak identification with applications in macroeconomic models. In other work, they proposed an innovative approach to inference in a large class of models, based on a novel summary of identification strength.

More recently, Andrews and a collaborator examined publication bias in economics and other disciplines. Understanding that there is a preference for empirical papers with statistically significant results, the authors propose a conceptual framework for estimating the probability of publication as a function of the results of the study, using either replication studies or meta-analyses in their approach. Other recent work considers how methods perform when a model is incorrect and studies how to better convey scientific information to decision makers. A highly productive researcher and generous collaborator, Andrews demonstrates keen insight into how to address key statistical challenges in econometrics that are directly useful to empirical economists and have relevance to multiple fields.

BIOGRAPHY
Isaiah Andrews received a BA (2009) from Yale University and a PhD (2014) from the Massachusetts Institute of Technology. From 2016 to 2018, he served as the Silverman (1968) Family Career Assistant Professor and Associate Professor at the Massachusetts Institute of Technology. He joined the faculty of Harvard University in 2018, where he is a professor in the Department of Economics and a research associate at the National Bureau of Economic Research. His publications have appeared in such journals as American Economic Review, Econometrica, and Quarterly Journal of Economics, among others.



以赛亚-安德鲁
计量经济学家 | 2020级
开发稳健的统计推断方法，以解决经济学和社会科学中的关键挑战。


以赛亚-安德鲁的肖像

标题
计量经济学家
工作单位
哈佛大学经济系
工作地点
马萨诸塞州剑桥市
年龄
获奖时34岁
重点领域
经济学
网站
哈佛大学。以赛亚-安德鲁
发表于2020年10月6日
关于以赛亚的工作
以赛亚-安德鲁是一位计量经济学家，他正在开发可靠且广泛适用的统计推断方法，以应对经济学、社会科学和医学领域的关键挑战。统计推断是一个过程，根据从人口中抽取的数据样本，推断出人口的属性。在不同的研究领域，安德鲁斯与不同的合作者合作，结合微分几何和决策理论的思想，建立技术上严格的统计推断方法，克服应用经验工作中遇到的常见问题。

安德鲁斯的大部分工作都集中在弱识别的问题上，在这种情况下，信息是有限的，许多标准推断程序的有效性因此受到质疑。如果一个变量对另一个变量的因果效应可以从足够大的数据集中得知，那么就可以说它是被识别的。当数据分布的一个小变化会消除我们确定因果效应的能力时，就会出现弱识别。在对非线性模型的研究中，安德鲁斯和一位合作者对弱识别的几何结构进行了突破性的分析，并在宏观经济模型中进行了应用。在其他工作中，他们提出了一种创新的方法来推断一大类模型，基于对识别强度的新颖总结。

最近，安德鲁斯和一位合作者研究了经济学和其他学科的出版偏见。了解到人们对具有统计学意义的经验性论文的偏爱，作者提出了一个概念框架，用于估计作为研究结果的函数的发表概率，在他们的方法中使用复制研究或元分析。最近的其他工作考虑了模型不正确时方法的表现，并研究了如何更好地向决策者传达科学信息。作为一个高产的研究者和慷慨的合作者，安德鲁斯对如何解决计量经济学中的关键统计挑战表现出敏锐的洞察力，这些挑战对经验经济学家直接有用，并与多个领域相关。

个人简历
以赛亚-安德鲁斯在耶鲁大学获得学士学位（2009年），在麻省理工学院获得博士学位（2014年）。从2016年到2018年，他在麻省理工学院担任西尔弗曼（1968）家族职业助理教授和副教授。他于2018年加入哈佛大学的教师队伍，在那里他是经济系的教授和国家经济研究局的研究助理。他的著作出现在《美国经济评论》、《计量经济学》和《经济学季刊》等杂志上。