Simple Tests for Selection: Learning More from Instrumental Variables
在局部平均处理效应框架下，提出检验未观测变量选择性的简单方法，可评估选择效应大小，适用于标准工具变量、不完全依从实验和模糊回归不连续设计，并用三个实证应用展示其价值。
We provide simple tests for selection on unobserved variables in the Vytlacil-Imbens-Angrist framework for Local Average Treatment Effects (LATEs). Our setup allows researchers not only to test for selection on either or both of the treated and untreated outcomes, but also to assess the magnitude of the selection effect. We show that it applies to the standard binary instrument case, as well as to experiments with imperfect compliance and fuzzy regression discontinuity designs, and we link it to broader discussions regarding instrumental variables. We illustrate the substantive value added by our framework with three empirical applications drawn from the literature.