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This paper discusses endogenous treatment models with duration outcomes,competing risks and random right censoring. The endogeneity issue is solvedusing a discrete instrumental variable. We show that the competing risks modelgenerates a non-parametric quantile instrumental regression problem. Thecause-specific cumulative incidence, the cause-specific hazard and thesubdistribution hazard can be recovered from the regression function. Adistinguishing feature of the model is that censoring and competing risksprevent identification at some quantiles. We characterize the set of quantilesfor which exact identification is possible and give partial identificationresults for other quantiles. We outline an estimation procedure and discuss itsproperties. The finite sample performance of the estimator is evaluated throughsimulations. We apply the proposed method to the Health Insurance Plan ofGreater New York experiment.

A nonparametric instrumental approach to endogeneity in competing risks models