We propose a semiparametric test to evaluate (i) whether different instruments induce subpopulations of compliers with the same observable characteristics on average, and (ii) whether compliers have observable characteristics that are the same as the full population on average. The test is a flexible robustness check for the external validity of instruments. We use it to reinterpret the difference in LATE estimates that Angrist and Evans (1998) obtain when using different instrumental variables. To justify the test, we characterize the doubly robust moment for Abadie (2003)'s class of complier parameters, and we analyze a machine learning update to $\kappa$ weighting.

翻译：我们建议进行半参数测试,以评估(一) 不同的仪器是否平均诱发具有相同可观测特性的遵守者亚群,以及(二) 遵守者是否有与平均全部人口相同的可观测特征。测试是对仪器外部有效性的灵活稳健检查。我们用它来重新解释LATE估计Angrist和Evans(1998年)在使用不同工具变量时获得的差异。为了证明这一测试的合理性,我们给Abadie(2003年)的级遵守者参数定了双重强势的时刻,我们分析了机器学习更新到$\kappa$加权的情况。