摘要翻译：
多值治疗模型通常是在限制性假设下研究的：有序选择，以及最近的无序单调性。我们展示了如何在一个更一般的模型类别中识别治疗效果，该模型允许多维未观察到的异质性。我们的结果依赖于两个主要假设：治疗分配必须是阈值交叉规则的可测量函数，并且必须有足够的连续仪器可用。我们对几类模型说明了我们的方法。
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英文标题：
《Identifying Effects of Multivalued Treatments》
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作者：
Sokbae Lee, Bernard Salani\'e
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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：
  Multivalued treatment models have typically been studied under restrictive assumptions: ordered choice, and more recently unordered monotonicity. We show how treatment effects can be identified in a more general class of models that allows for multidimensional unobserved heterogeneity. Our results rely on two main assumptions: treatment assignment must be a measurable function of threshold-crossing rules, and enough continuous instruments must be available. We illustrate our approach for several classes of models.
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PDF链接：
https://arxiv.org/pdf/1805.00057