A nonparametric instrumental approach to confounding in competing risks models.

This paper discusses nonparametric identification and estimation of the causal effect of a treatment in the presence of confounding, competing risks and random right-censoring. Our identification strategy is based on an instrumental variable. We show that the competing risks model generates a nonparametric quantile instrumental regression problem. Quantile treatment effects on the subdistribution function 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.

Discussion on "Instrumented difference-in-differences" by Ting Ye, Ashkan Ertefaie, James Flory, Sean Hennessy, and Dylan S. Small.

We discuss Ye et al. 2022, which combines instrumental variables methods with difference in differences. First, we compare the paper to other works in the difference in differences literatures and argue that the main contribution lies in the multiply robust estimation approach. Then, we reformulate the causal assumptions in Ye et al. 2022 in the usual theoretical framework of the instrumental variables literature. This clarifies in which sense the difference in differences design can weaken the standard instrumental variable conditions.