The State of Applied Econometrics: Causality and Policy Evaluation
The State of Applied Econometrics: Causality and Policy Evaluation
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应用计量经济学现状：因果关系与政策评估
DOI：
10.1257/jep.31.2.3
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发表时间：
2017-03-01
影响因子：
8.4
通讯作者：
Imbens, Guido W.
中科院分区：
文献类型：
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作者：
Athey, Susan;Imbens, Guido W.
在本文中，我们讨论了计量经济学的最新发展，我们认为这对研究政策评估问题的实证研究人员很重要。我们专注于三个主要领域，在每种情况下，突出应用工作的建议。首先，我们讨论了新的研究识别策略的程序评估，特别是集中在综合控制方法，回归不连续性，外部有效性，回归方法的因果解释。其次，我们讨论了各种形式的补充分析，包括安慰剂分析以及敏感性和稳健性分析，旨在使识别策略更可信。第三，我们讨论了机器学习方法的最新进展对因果效应的影响，包括在高维环境中调整治疗和控制单元之间差异的方法，以及识别和估计异质性治疗效应的方法。
In this paper, we discuss recent developments in econometrics that we view as important for empirical researchers working on policy evaluation questions. We focus on three main areas, in each case, highlighting recommendations for applied work. First, we discuss new research on identification strategies in program evaluation, with particular focus on synthetic control methods, regression discontinuity, external validity, and the causal interpretation of regression methods. Second, we discuss various forms of supplementary analyses, including placebo analyses as well as sensitivity and robustness analyses, intended to make the identification strategies more credible. Third, we discuss some implications of recent advances in machine learning methods for causal effects, including methods to adjust for differences between treated and control units in high-dimensional settings, and methods for identifying and estimating heterogenous treatment effects.