Causal inference in regression: advice to authors

IF 3.6 3区 哲学 0 RELIGION
Joseph A. Bulbulia, U. Schjoedt, J. Shaver, R. Sosis, W. Wildman
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引用次数: 5

Abstract

The 2021 Nobel prize in economics was awarded to David Card, Joshua Angrist, and Guido Imbens. Card, together with his PhD supervisor the late Alan Krueger, developed empirical methods for investigating how policy interventions affect labor markets. Angrist and Imbens developed methods for identifying causes from real-world complexity. Collectively, this work on causal inference has come to redefine how economists conduct research. A parallel storey for the emergence and growth of causal methods unfolded a quarter-century earlier in the discipline of epidemiology (Hill, 1965). Formal methods for causal inference trace an even longer history, beginning with the work of Sewall Wright on biological development and inheritance (Wright, 1921, 1923, 1934). Most empirical research published in Religion, Brain & Behavior is produced by scientists working in psychology, a field in which methods for causal inference remain poorly developed (see Rohrer, 2018). Here, we offer advice to RBB authors hoping to address causal inference using regression, ANOVA, and structural equation modeling.
回归中的因果推理:给作者的建议
2021年诺贝尔经济学奖被授予大卫·卡德、约书亚·安格里斯特和圭多·因本斯。卡德和他的博士生导师、已故的艾伦·克鲁格(Alan Krueger)共同开发了实证方法,用于调查政策干预如何影响劳动力市场。Angrist和Imbens开发了从现实世界的复杂性中识别原因的方法。总的来说,这种因果推理的工作已经重新定义了经济学家如何进行研究。早在四分之一个世纪之前,在流行病学学科中,因果方法的出现和发展就展开了一个平行的故事(Hill, 1965)。因果推理的形式方法可以追溯到更长的历史,始于Sewall Wright关于生物发育和遗传的工作(Wright, 1921, 1923, 1934)。发表在《宗教、大脑与行为》上的大多数实证研究都是由心理学领域的科学家进行的,在这个领域,因果推理的方法仍然很不发达(见Rohrer, 2018)。在这里,我们为希望使用回归、方差分析和结构方程模型解决因果推理的RBB作者提供建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.00
自引率
13.60%
发文量
93
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