摘要翻译：
治疗分配与潜在结果的条件独立性是一个常用但不可辩驳的假设。从这个条件独立性假设出发，我们导出了各种治疗效果参数在非参数偏差下的识别集。这些偏差是通过条件处理分配概率定义的，这使得解释变得简单。我们的结果可以用来评估在基线条件独立性假设下得到的经验结论的稳健性。
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英文标题：
《Identification of Treatment Effects under Conditional Partial
  Independence》
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作者：
Matthew A. Masten and Alexandre Poirier
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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：
  Conditional independence of treatment assignment from potential outcomes is a commonly used but nonrefutable assumption. We derive identified sets for various treatment effect parameters under nonparametric deviations from this conditional independence assumption. These deviations are defined via a conditional treatment assignment probability, which makes it straightforward to interpret. Our results can be used to assess the robustness of empirical conclusions obtained under the baseline conditional independence assumption.
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PDF链接：
https://arxiv.org/pdf/1707.09563