Instrument Validity Tests With Causal Forests

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.

能够识别局部平均处理效应（local average treatment effects, LATE）的假设，会生成可用于证伪工具变量有效性的必要条件。然而，工具变量有效性被违背的程度，大概率会因不同亚人群体而存在差异。本文采用因果森林（causal forests），以数据驱动的方式搜索并检验LATE假设的此类局部违背情形。与既往的工具变量有效性检验方法不同，我们提出的检验流程能够检测出局部性的违背情况。我们通过模拟实验评估了所提流程的性能，并将其应用于两类不同场景：父母对子女混合性别构成的偏好，以及越南征兵抽签。