Heckman James, Pinto Rodrigo
The University of Chicago, Department of Economics, 1126 E. 59 St., Chicago, IL 60637.
University of South Florida, Department of Economics, Arts and Sciences Multidisciplinary Complex, Tampa, FL 33620.
J Econom. 2024 Jul;243(1-2). doi: 10.1016/j.jeconom.2024.105719. Epub 2024 Apr 8.
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.
本文考察了由拉格纳·弗里希和特里夫·哈维尔莫提出的计量经济学因果模型以及基于思想实验的经验证据解释。我们将计量经济学因果模型与当前两种流行的因果框架进行比较：奈曼-鲁宾因果模型和因果计算。奈曼-鲁宾因果模型基于潜在结果的语言，主要由统计学家发展而来。它并非基于思想实验，而是以统计实验为基础。由朱迪亚·珀尔及其合著者提出的因果计算依赖于有向无环图（DAG），是计算机科学和应用数学中一种流行的因果框架。我们认为，不加批判地使用这些框架的经济学家往往会摒弃计量经济学因果模型的诸多益处，从而不利于进行更具信息性的分析。我们使用经济学中发展出的因果模型来说明计量经济学框架的通用性和能力。
