This paper studies treatment effect models in which individuals are classified into unobserved groups based on heterogeneous treatment rules. Using a finite mixture approach, we propose a marginal treatment effect (MTE) framework in which the treatment choice and outcome equations can be heterogeneous across groups. Under the availability of instrumental variables specific to each group, we show that the MTE for each group can be separately identified. Based on our identification result, we propose a two-step semiparametric procedure for estimating the group-wise MTE. We illustrate the usefulness of the proposed method with an application to economic returns to college education.

翻译：本文研究个人根据不同待遇规则被归类为未观察群体的治疗效果模型。我们采用有限的混合法,提出一个边际治疗效果框架,其中处理选择和结果方程式可以各群体之间有差异。根据每个群体特有的工具变量,我们表明每个群体可分别确定MTE。根据我们的识别结果,我们提出一个分两步的半参数程序,用于估计群体MTE。我们说明了拟议方法的有用性,将经济回报应用于大学教育。