摘要翻译：
在不混淆治疗分配下的平均治疗效果的半参数估计方面有大量的文献，这些估计是在具有固定数量协变量的情况下进行的。最近，注意力集中在具有大量协变量的设置上。在本文中，我们从早期文献中扩展到这种新的设置。我们建议，除了报告点估计数和标准误差外，研究人员还报告一些补充分析的结果，以帮助评估他们估计数的可信度。
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英文标题：
《Estimating Average Treatment Effects: Supplementary Analyses and
  Remaining Challenges》
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作者：
Susan Athey, Guido Imbens, Thai Pham, Stefan Wager
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最新提交年份：
2017
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分类信息：

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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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：
  There is a large literature on semiparametric estimation of average treatment effects under unconfounded treatment assignment in settings with a fixed number of covariates. More recently attention has focused on settings with a large number of covariates. In this paper we extend lessons from the earlier literature to this new setting. We propose that in addition to reporting point estimates and standard errors, researchers report results from a number of supplementary analyses to assist in assessing the credibility of their estimates.
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PDF链接：
https://arxiv.org/pdf/1702.01250