Instrument Validity Tests With Causal Forests
利用因果森林方法，以数据驱动的方式检验工具变量在局部子群体中的有效性，能够发现传统检验无法识别的局部违反LATE假设的情况。
Assumptions that are sufficient to identify local average treatment effects (LATEs) generate necessary conditions that allow instrument validity to be refuted. The degree to which instrument validity is violated, however, probably varies across subpopulations. In this article, we use causal forests to search and test for such local violations of the LATE assumptions in a data-driven way. Unlike previous instrument validity tests, our procedure is able to detect local violations. We evaluate the performance of our procedure in simulations and apply it in two different settings: parental preferences for mixed-sex composition of children and the Vietnam draft lottery.