  Methods for estimating heterogeneous treatment effects (HTE) from
observational data have largely focused on continuous or binary outcomes, with
less attention paid to survival outcomes and almost none to settings with
competing risks. In this work, we develop censoring unbiased transformations
(CUTs) for survival outcomes both with and without competing risks.After
converting time-to-event outcomes using these CUTs, direct application of HTE
learners for continuous outcomes yields consistent estimates of heterogeneous
cumulative incidence effects, total effects, and separable direct effects. Our
CUTs enable application of a much larger set of state of the art HTE learners
for censored outcomes than had previously been available, especially in
competing risks settings. We provide generic model-free learner-specific oracle
inequalities bounding the finite-sample excess risk. The oracle efficiency
results depend on the oracle selector and estimated nuisance functions from all
steps involved in the transformation. We demonstrate the empirical performance
of the proposed methods in simulation studies.
