摘要翻译：
当研究人员希望使用多个工具变量进行单一的二元处理时，熟悉的后期单调性假设可能会变得限制性：它要求所有单元共享一个共同的反应方向，即使不同的工具在相反的方向上移动。相比之下，我所说的矢量单调性只是限制治疗状态在每个仪器中单独单调。在许多情况下，这是一个自然的假设，抓住了每个工具“没有defiers”的直觉概念。我证明了在一个二元处理和多个离散仪器的环境中，一类因果参数在向量单调性下被点识别，包括对仪器的任何特定子集响应的单元之间的平均处理效果。我提出了一个简单的“类2SLS”估计的家庭识别的治疗效果参数。一个实证应用重新审视劳动力市场对大学教育的回报。
---
英文标题：
《A Vector Monotonicity Assumption for Multiple Instruments》
---
作者：
Leonard Goff
---
最新提交年份：
2020
---
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。
--

---
英文摘要：
  When a researcher wishes to use multiple instrumental variables for a single binary treatment, the familiar LATE monotonicity assumption can become restrictive: it requires that all units share a common direction of response even when different instruments are shifted in opposing directions. What I call vector monotonicity, by contrast, simply restricts treatment status to be monotonic in each instrument separately. This is a natural assumption in many contexts, capturing the intuitive notion of "no defiers" for each instrument. I show that in a setting with a binary treatment and multiple discrete instruments, a class of causal parameters is point identified under vector monotonicity, including the average treatment effect among units that are responsive to any particular subset of the instruments. I propose a simple "2SLS-like" estimator for the family of identified treatment effect parameters. An empirical application revisits the labor market returns to college education.
---
PDF链接：
https://arxiv.org/pdf/2009.00553