论文标题
异质系数，控制变量和多种治疗效果的识别
Heterogeneous Coefficients, Control Variables, and Identification of Multiple Treatment Effects
论文作者
论文摘要
多维异质性和内生性是具有多种处理的模型的重要特征。我们考虑了一个异质系数模型，其中结果是虚拟处理变量的线性组合，每个变量代表不同类型的处理。我们使用控制变量为鉴定平均治疗效果提供必要和充分的条件。通过互斥的处理，我们发现，只要异质系数是平均与鉴于对照的处理的平均值，简单的识别条件是，将广义倾向得分（Imbens，2000）远离零界限，并且它们的总和与一个概率限制为概率。我们的分析扩展到分布和分位治疗效果，以及对治疗的相应治疗效果。这些结果概括了Rosenbaum和Rubin（1983）用于二元处理的经典鉴定结果。
Multidimensional heterogeneity and endogeneity are important features of models with multiple treatments. We consider a heterogeneous coefficients model where the outcome is a linear combination of dummy treatment variables, with each variable representing a different kind of treatment. We use control variables to give necessary and sufficient conditions for identification of average treatment effects. With mutually exclusive treatments we find that, provided the heterogeneous coefficients are mean independent from treatments given the controls, a simple identification condition is that the generalized propensity scores (Imbens, 2000) be bounded away from zero and that their sum be bounded away from one, with probability one. Our analysis extends to distributional and quantile treatment effects, as well as corresponding treatment effects on the treated. These results generalize the classical identification result of Rosenbaum and Rubin (1983) for binary treatments.
