摘要翻译：
我们发展了一种新的方法来估计在观察性研究中的平均治疗效果与未观察到的组水平异质性。在这样的设置中，一种常见的方法是使用最小二乘回归估计的线性固定效应规范。这些方法严格地限制了组间异质性的程度，因为它们作出了限制性的假设，即线性调整组间平均协变量值的差异可以解决跨组比较的所有问题。我们从两个观察开始。首先，我们注意到，效应中的固定效应方法只通过调整协变量值的平均值和平均处理来调整组间的差异。其次，我们注意到，在固定效应设置下，通过倾向得分的逆加权将消除治疗和控制单元之间的比较偏差。然后，基于这两个观测结果，我们发展了固定效应方法的三个推广。首先，我们建议对平均协变量值进行更一般的非线性调整。其次，我们建议通过使用倾向评分加权来稳健估计量。第三，我们激励和开发调整的实现，这些调整也适用于超过平均协变量值的群体特征。
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英文标题：
《The Role of the Propensity Score in Fixed Effect Models》
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作者：
Dmitry Arkhangelsky, Guido Imbens
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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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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  We develop a new approach for estimating average treatment effects in the observational studies with unobserved group-level heterogeneity. A common approach in such settings is to use linear fixed effect specifications estimated by least squares regression. Such methods severely limit the extent of the heterogeneity between groups by making the restrictive assumption that linearly adjusting for differences between groups in average covariate values addresses all concerns with cross-group comparisons. We start by making two observations. First we note that the fixed effect method in effect adjusts only for differences between groups by adjusting for the average of covariate values and average treatment. Second, we note that weighting by the inverse of the propensity score would remove biases for comparisons between treated and control units under the fixed effect set up. We then develop three generalizations of the fixed effect approach based on these two observations. First, we suggest more general, nonlinear, adjustments for the average covariate values. Second, we suggest robustifying the estimators by using propensity score weighting. Third, we motivate and develop implementations for adjustments that also adjust for group characteristics beyond the average covariate values.
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PDF链接：
https://arxiv.org/pdf/1807.02099