Heterogeneous coefficients, control variables and identification of multiple treatment effects
Heterogeneous coefficients, control variables and identification of multiple treatment effects
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异质系数、控制变量和多种治疗效果的识别
DOI：
10.1093/biomet/asab060
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发表时间：
2021
期刊：
影响因子：
2.7
通讯作者：
Stouli, S
中科院分区：
文献类型：
--
作者：
Newey, W K;Stouli, S
多维异质性和内生性是多处理模型的重要特征。我们考虑一个异质性系数模型，其中结果是虚拟治疗变量的线性组合，每个变量代表一种不同的治疗。利用控制变量给出了判别平均处理效果的充分必要条件。通过互斥处理，我们发现，假设异质性系数与给定控制的处理平均独立，一个简单的识别条件是广义倾向得分有界于零，它们的和有界于一，概率为1。我们的分析扩展到分布效应和分位数效应，以及相应的治疗对被治疗者的影响。这些结果推广了二元处理的经典鉴别结果。
Multi-dimensional 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 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 for binary treatments.