Decomposing identification gains and evaluating instrument identification power for partially identified average treatment effects
综合回顾了工具变量在平均处理效应部分识别中的作用，提出识别增益的新分解方法，并通过生育与女性劳动供给的实例展示不同工具变量的识别力差异。
.In this article, we synthesize and review existing results on the roles of instrumental variables (IVs) in average treatment effect (ATE) partial identification analysis. We provide a novel decomposition of identification gains in ATE bounds and offer insights for understanding the complex role of IVs in conjunction with model features and covariates. An empirical example of childbearing and women’s labor supply, with two IVs of ‘twins’ and ‘same-sex siblings’, demonstrates that ‘twins’ has significantly greater identification power than ‘same-sex siblings’, and the identification power of both IVs is heterogeneous across covariates. Our analysis can also be useful in IV selection in future program experiment designs.