This paper discusses endogenous treatment models with duration outcomes, competing risks and random right censoring. The endogeneity issue is solved using a discrete instrumental variable. We show that the competing risks model generates a non-parametric quantile instrumental regression problem. The cause-specific cumulative incidence, the cause-specific hazard and the subdistribution hazard can be recovered from the regression function. A distinguishing feature of the model is that censoring and competing risks prevent identification at some quantiles. We characterize the set of quantiles for which exact identification is possible and give partial identification results for other quantiles. We outline an estimation procedure and discuss its properties. The finite sample performance of the estimator is evaluated through simulations. We apply the proposed method to the Health Insurance Plan of Greater New York experiment.

翻译：本文讨论内生处理模式,包括持续时间结果、相互竞争的风险和随机右审查。内生性问题通过一个独立的工具变量来解决。我们表明,相互竞争的风险模式产生了一个非参数量化工具回归问题。具体原因的累积发生率、特定原因的危害和次分配危害可以从回归功能中恢复。模型的一个显著特征是,检查和相互竞争的风险无法在某些四分位中识别。我们描述能够准确识别的一组量化,并为其他量化提供部分识别结果。我们概述了一个估算程序并讨论了其属性。通过模拟对估算器的有限样本性能进行了评估。我们将拟议方法应用于大纽约医疗保险计划实验。