mlsanityData to 7 Oct 2026

Estimating heterogeneous treatment effects with right-censored data via causal survival forests

arXiv 2001.09887 ↗First posted 27 Jan 2020stat.ME cs.LG stat.MLcited by 29

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Abstract (arXiv)

Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation. Building on this line of work, we introduce causal survival forests, which can be used to estimate heterogeneous treatment effects in a survival and observational setting where outcomes may be right-censored. Our approach relies on orthogonal estimating equations to robustly adjust for both censoring and selection effects under unconfoundedness. In our experiments, we find our approach to perform well relative to a number of baselines.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jan 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 6; 2023: 5; 2024: 2; 2025: 9; 2026: 5.

29 papers.

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  1. 2021Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effectscited by 29
  2. 2021SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Datacited by 25
  3. 2025Doubly Robust Alignment for Large Language Modelscited by 11
  4. 2025Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Datacited by 10
  5. 2023Positivity-free Policy Learning with Observational Datacited by 6
  6. 2022Calibration Error for Heterogeneous Treatment Effectscited by 5
  7. 2023Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival datacited by 5
  8. 2022What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?cited by 4
  9. 2022Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forestscited by 3
  10. 2022SurvCaus : Representation Balancing for Survival Causal Inferencecited by 2
  11. 2022BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effectcited by 1
  12. 2023Should I Stop or Should I Go: Early Stopping with Heterogeneous Populationscited by 1
17 more
  1. 2024Learning Robust Treatment Rules for Censored Datacited by 1
  2. 2025Debiased maximum-likelihood estimators for hazard ratios under kernel-based machine-learning adjustmentcited by 1
  3. 2025Horseshoe Forests for High-Dimensional Causal Survival Analysiscited by 1
  4. 2025Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoringcited by 1
  5. 2022Confidence Band Estimation for Survival Random Forestscited by 0
  6. 2023Generalized Random Forests using Fixed-Point Treescited by 0
  7. 2023A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effectscited by 0
  8. 2024Individualized Multi-Treatment Response Curves Estimation using RBF-net with Shared Neuronscited by 0
  9. 2025Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Treescited by 0
  10. 2025CAST: Time-Varying Treatment Effects with Application to Chemotherapy and Radiotherapy on Head and Neck Squamous Cell Carcinomacited by 0
  11. 2025The Impact of Medication Non-adherence on Adverse Outcomes: Evidence from Schizophrenia Patients via Survival Analysiscited by 0
  12. 2025Synthetic Survival Control: Extending Synthetic Controls for "When-If" Decisioncited by 0
  13. 2026Nonparametric Regression Discontinuity Designs with Survival Outcomescited by 0
  14. 2026Estimating heterogeneous treatment effects with survival outcomes via a deep survival learnercited by 0
  15. 2026Adaptive Experimentation for Censored Survival Outcomescited by 0
  16. 2026Deep Optimal Individualized Treatment Rules for Bivariate Survival Outcomes via Adaptive Prediction-Powered Learningcited by 0
  17. 2026Surv-IPTB: An Attention-Based Model for Estimating Individual Probability of Treatment Benefit with Survival Datacited by 0