计量经济学的因果关系:思想实验的核心作用

IF 9.9 3区 经济学 Q1 ECONOMICS Journal of Econometrics Pub Date : 2024-07-01 DOI:10.1016/j.jeconom.2024.105719
James Heckman , Rodrigo Pinto
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引用次数: 0

摘要

本文研究了拉格纳-弗里施(Ragnar Frisch)和特里格夫-哈维尔莫(Trygve Haavelmo)基于思想实验开发的计量经济学因果模型和对经验证据的解释。我们将计量经济学因果模型与目前流行的两个因果框架进行了比较:奈曼-鲁宾因果模型和 Do-Calculus 因果模型。奈曼-鲁宾因果模型基于潜在结果语言,主要由统计学家开发。它不是以思想实验为基础,而是以统计实验为基础。由 Judea Pearl 和合著者开发的 Do-Calculus 依赖于有向无环图 (DAG),是计算机科学和应用数学领域流行的因果框架。我们提出的理由是,不加批判地使用这些框架的经济学家往往会丢弃计量经济学因果模型的巨大优势,从而不利于进行更有价值的分析。我们使用经济学中开发的因果模型来说明计量经济学框架的多功能性和能力。
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Econometric causality: The central role of thought experiments

This paper examines the econometric causal model and the interpretation of empirical evidence based on thought experiments that was developed by Ragnar Frisch and Trygve Haavelmo. We compare the econometric causal model with two currently popular causal frameworks: the Neyman–Rubin causal model and the Do-Calculus. The Neyman–Rubin causal model is based on the language of potential outcomes and was largely developed by statisticians. Instead of being based on thought experiments, it takes statistical experiments as its foundation. The Do-Calculus, developed by Judea Pearl and co-authors, relies on Directed Acyclic Graphs (DAGs) and is a popular causal framework in computer science and applied mathematics. We make the case that economists who uncritically use these frameworks often discard the substantial benefits of the econometric causal model to the detriment of more informative analyses. We illustrate the versatility and capabilities of the econometric framework using causal models developed in economics.

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来源期刊
Journal of Econometrics
Journal of Econometrics 社会科学-数学跨学科应用
CiteScore
8.60
自引率
1.60%
发文量
220
审稿时长
3-8 weeks
期刊介绍: The Journal of Econometrics serves as an outlet for important, high quality, new research in both theoretical and applied econometrics. The scope of the Journal includes papers dealing with identification, estimation, testing, decision, and prediction issues encountered in economic research. Classical Bayesian statistics, and machine learning methods, are decidedly within the range of the Journal''s interests. The Annals of Econometrics is a supplement to the Journal of Econometrics.
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