摘要翻译：
本文研究了一个面板数据设置，目标是通过预测治疗单位的结果的反事实值来估计干预的因果效应，如果他们没有接受治疗的话。针对这一问题，人们提出了几种方法，包括回归法、综合控制法和矩阵补全法。本文考虑了一种集成方法，并表明在几个经济数据集上，它比任何单独的方法都有更好的性能。矩阵完成方法通常被集合赋予最大的权重，但这显然取决于设置。我们认为集合方法为因果面板数据的进一步研究提供了一个富有成效的方向。
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英文标题：
《Ensemble Methods for Causal Effects in Panel Data Settings》
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作者：
Susan Athey, Mohsen Bayati, Guido Imbens, and Zhaonan Qu
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  This paper studies a panel data setting where the goal is to estimate causal effects of an intervention by predicting the counterfactual values of outcomes for treated units, had they not received the treatment. Several approaches have been proposed for this problem, including regression methods, synthetic control methods and matrix completion methods. This paper considers an ensemble approach, and shows that it performs better than any of the individual methods in several economic datasets. Matrix completion methods are often given the most weight by the ensemble, but this clearly depends on the setting. We argue that ensemble methods present a fruitful direction for further research in the causal panel data setting.
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PDF链接：
https://arxiv.org/pdf/1903.10079