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摘要翻译：
人们应该如何评估比统计独立性弱的假设的可信度，比如分位数独立性？在确定治疗变量的因果效应的背景下，我们认为这种偏差应该根据它们允许的不可观察的选择形式来选择。对于分位数独立性，我们刻画了这种形式的处理选择。具体地说，我们证明分位数独立性等价于对潜在倾向分数（对于二元处理）或给定不可观察性的处理cdf平均值的约束（对于连续处理）。在这两种情况下，这个平均值约束都需要一种非单调的处理选择。利用这些结果，我们表明几种常见的处理选择模型与分位数独立性是不相容的。我们引入了一类假设，通过消除平均值约束来削弱分位数独立性，从而允许单调处理选择。在一个二元处理的潜在结果模型中，我们在两类假设下导出了ATT和QTT的识别集。在一个数值例子中，我们证明了分位数独立性所固有的平均值约束具有很强的识别能力。我们的结果提示研究者在用分位数独立性来削弱全独立性时，应该仔细考虑这种非单调性性质的可信度。
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英文标题：
《Interpreting Quantile Independence》
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作者：
Matthew A. Masten and Alexandre Poirier
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最新提交年份：
2018
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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：
How should one assess the credibility of assumptions weaker than statistical independence, like quantile independence? In the context of identifying causal effects of a treatment variable, we argue that such deviations should be chosen based on the form of selection on unobservables they allow. For quantile independence, we characterize this form of treatment selection. Specifically, we show that quantile independence is equivalent to a constraint on the average value of either a latent propensity score (for a binary treatment) or the cdf of treatment given the unobservables (for a continuous treatment). In both cases, this average value constraint requires a kind of non-monotonic treatment selection. Using these results, we show that several common treatment selection models are incompatible with quantile independence. We introduce a class of assumptions which weakens quantile independence by removing the average value constraint, and therefore allows for monotonic treatment selection. In a potential outcomes model with a binary treatment, we derive identified sets for the ATT and QTT under both classes of assumptions. In a numerical example we show that the average value constraint inherent in quantile independence has substantial identifying power. Our results suggest that researchers should carefully consider the credibility of this non-monotonicity property when using quantile independence to weaken full independence.
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PDF链接：
https://arxiv.org/pdf/1804.10957
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