Abstract: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.
From: Oliver Lunding Sandqvist [view email]
[v1]
Thu, 7 Nov 2024 17:48:54 UTC (1,033 KB)
[v2]
Sat, 1 Aug 2026 12:13:01 UTC (1,037 KB)
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