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Title: Doubly robust nonparametric instrumental variable estimators for censored outcomes Authors:  Nandita Mitra - University of Pennsylvania (United States) [presenting]
Edward Kennedy - Carnegie Mellon University (United States)
Youjin Lee - University of Pennsylvania (United States)
Abstract: Instrumental variable (IV) methods allow us the opportunity to address unmeasured confounding in observational studies and randomized studies with noncompliance. However, there are very few IV methods for censored survival outcomes. We propose nonparametric estimators for the local average treatment effect on survival probabilities under both nonignorable and ignorable censoring. We provide an efficient influence function-based estimator and a simple estimation procedure when the IV is either binary or continuous. The proposed estimators possess double-robustness properties and can easily incorporate nonparametric estimation using machine learning tools. In simulation studies, we demonstrate the flexibility and efficiency of our proposed estimators under various plausible scenarios. We apply our method to the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial to estimate the causal effect of screening on survival probabilities and estimate causal contrasts between two interventions under different censoring assumptions.