A Doubly Robust Learner for Regression and Inference With Right‐Censored Outcomes

This paper introduces a general framework for doubly robust nonparametric regression with right‐censored outcomes, adapting the strategy of the “DR‐learner” for heterogeneous treatment effects to the censored data setting. Our method generalizes censoring unbiased transformations to generate pseudo‐outcomes, which serve as inputs for a second‐stage nonparametric regression with sample‐splitting or cross‐fitting. We derive a novel representation of the conditional bias of these pseudo‐outcomes; this representation is central to establishing the estimator's asymptotic properties, including rate double robustness and oracle efficiency when the second‐stage regression is a linear smoother. Simulation studies demonstrate the method's favorable finite‐sample performance, and we showcase its practical utility by applying it to estimate a causal effect within a regression discontinuity design with right‐censored outcomes.