Double robustness for complier parameters and a semi-parametric test for complier characteristics
提出一个半参数检验，评估不同工具变量是否产生具有相同可观测特征的依从者子总体，以及依从者特征是否与总体、处理组或未处理组相同，作为工具变量外部有效性的灵活稳健性检验。
Summary We propose a semi-parametric test to evaluate (a) whether different instruments induce subpopulations of compliers with the same observable characteristics, on average; and (b) whether compliers have observable characteristics that are the same as the full population, treated subpopulation, or untreated subpopulation, on average. The test is a flexible robustness check for the external validity of instruments. To justify the test, we characterise the doubly robust moment for Abadie’s class of complier parameters, and we analyse a machine learning update to weighting that we call the automatic $\kappa$ weight. We use the test to reinterpret Angrist and Evans' different local average treatment effect estimates obtained using different instrumental variables.