mlsanityData to 7 Oct 2026

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

arXiv 2401.11263 ↗First posted 20 Jan 2024stat.ME stat.MLcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 12 of its 117 references that are in the map. First posted on arXiv 20 Jan 2024.

Builds on

12 of 117 references.

References · 12

  1. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
  2. 2021SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Datacited by 25
  3. 2017Selective inference for effect modification via the lassocited by 12
  4. 2023On Weighted Orthogonal Learners for Heterogeneous Treatment Effectscited by 8
  5. 2021Estimating Heterogeneous Treatment Effects for General Responsescited by 7
  6. 2021On Doubly Robust Inference for Double Machine Learning in Semiparametric Regressioncited by 6
  7. 2023Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Datacited by 5
  8. 2023A nonparametric framework for treatment effect modifier discovery in high dimensionscited by 3
  9. 2020Estimating Structural Target Functions using Machine Learning and Influence Functionscited by 2
  10. 2020Deep Learning for Individual Heterogeneitycited by 2
  11. 2021One-step TMLE for targeting cause-specific absolute risks and survival curvescited by 2
  12. 2022SurvCaus : Representation Balancing for Survival Causal Inferencecited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Datacited by 10
  2. 2025Debiased maximum-likelihood estimators for hazard ratios under kernel-based machine-learning adjustmentcited by 1
  3. 2025Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoringcited by 1
  4. 2026Adaptive Experimentation for Censored Survival Outcomescited by 0