# Estimating Heterogeneous Treatment Effects on Survival Outcomes Using Counterfactual Censoring Unbiased Transformations

arXiv: https://arxiv.org/abs/2401.11263 · HTML: https://mlsanity.com/paper/2401.11263/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 117 references that are in the map.
- First posted on arXiv 20 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms](https://mlsanity.com/paper/2101.10943/) (2021)
- [SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data](https://mlsanity.com/paper/2110.14001/) (2021)
- [Selective inference for effect modification via the lasso](https://mlsanity.com/paper/1705.08020/) (2017)
- [On Weighted Orthogonal Learners for Heterogeneous Treatment Effects](https://mlsanity.com/paper/2303.12687/) (2023)
- [Estimating Heterogeneous Treatment Effects for General Responses](https://mlsanity.com/paper/2103.04277/) (2021)
- [On Doubly Robust Inference for Double Machine Learning in Semiparametric Regression](https://mlsanity.com/paper/2107.06124/) (2021)
- [Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data](https://mlsanity.com/paper/2302.12718/) (2023)
- [A nonparametric framework for treatment effect modifier discovery in high dimensions](https://mlsanity.com/paper/2304.05323/) (2023)
- [Estimating Structural Target Functions using Machine Learning and Influence Functions](https://mlsanity.com/paper/2008.06461/) (2020)
- [Deep Learning for Individual Heterogeneity](https://mlsanity.com/paper/2010.14694/) (2020)
- [One-step TMLE for targeting cause-specific absolute risks and survival curves](https://mlsanity.com/paper/2107.01537/) (2021)
- [SurvCaus : Representation Balancing for Survival Causal Inference](https://mlsanity.com/paper/2203.15672/) (2022)

## Built on by (most cited first, 4 of 4)

- [Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data](https://mlsanity.com/paper/2505.13072/) (2025)
- [Debiased maximum-likelihood estimators for hazard ratios under kernel-based machine-learning adjustment](https://mlsanity.com/paper/2507.17686/) (2025)
- [Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring](https://mlsanity.com/paper/2510.13397/) (2025)
- [Adaptive Experimentation for Censored Survival Outcomes](https://mlsanity.com/paper/2605.18459/) (2026)

## Abstract (arXiv)

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.