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

arXiv: https://arxiv.org/abs/2001.09887 · HTML: https://mlsanity.com/paper/2001.09887/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2020.

## Built on by (most cited first, 29 of 29)

- [Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects](https://mlsanity.com/paper/2111.07966/) (2021)
- [SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data](https://mlsanity.com/paper/2110.14001/) (2021)
- [Doubly Robust Alignment for Large Language Models](https://mlsanity.com/paper/2506.01183/) (2025)
- [Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data](https://mlsanity.com/paper/2505.13072/) (2025)
- [Positivity-free Policy Learning with Observational Data](https://mlsanity.com/paper/2310.06969/) (2023)
- [Calibration Error for Heterogeneous Treatment Effects](https://mlsanity.com/paper/2203.13364/) (2022)
- [Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data](https://mlsanity.com/paper/2301.05491/) (2023)
- [What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?](https://mlsanity.com/paper/2206.10323/) (2022)
- [Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests](https://mlsanity.com/paper/2210.02836/) (2022)
- [SurvCaus : Representation Balancing for Survival Causal Inference](https://mlsanity.com/paper/2203.15672/) (2022)
- [BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect](https://mlsanity.com/paper/2211.10793/) (2022)
- [Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations](https://mlsanity.com/paper/2306.11839/) (2023)
- [Learning Robust Treatment Rules for Censored Data](https://mlsanity.com/paper/2408.09155/) (2024)
- [Debiased maximum-likelihood estimators for hazard ratios under kernel-based machine-learning adjustment](https://mlsanity.com/paper/2507.17686/) (2025)
- [Horseshoe Forests for High-Dimensional Causal Survival Analysis](https://mlsanity.com/paper/2507.22004/) (2025)
- [Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring](https://mlsanity.com/paper/2510.13397/) (2025)
- [Confidence Band Estimation for Survival Random Forests](https://mlsanity.com/paper/2204.12038/) (2022)
- [Generalized Random Forests using Fixed-Point Trees](https://mlsanity.com/paper/2306.11908/) (2023)
- [A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects](https://mlsanity.com/paper/2310.02278/) (2023)
- [Individualized Multi-Treatment Response Curves Estimation using RBF-net with Shared Neurons](https://mlsanity.com/paper/2401.16571/) (2024)
- [Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees](https://mlsanity.com/paper/2502.01575/) (2025)
- [CAST: Time-Varying Treatment Effects with Application to Chemotherapy and Radiotherapy on Head and Neck Squamous Cell Carcinoma](https://mlsanity.com/paper/2505.06367/) (2025)
- [The Impact of Medication Non-adherence on Adverse Outcomes: Evidence from Schizophrenia Patients via Survival Analysis](https://mlsanity.com/paper/2506.18187/) (2025)
- [Synthetic Survival Control: Extending Synthetic Controls for "When-If" Decision](https://mlsanity.com/paper/2511.14133/) (2025)
- [Nonparametric Regression Discontinuity Designs with Survival Outcomes](https://mlsanity.com/paper/2604.03502/) (2026)
- [Estimating heterogeneous treatment effects with survival outcomes via a deep survival learner](https://mlsanity.com/paper/2604.10398/) (2026)
- [Adaptive Experimentation for Censored Survival Outcomes](https://mlsanity.com/paper/2605.18459/) (2026)
- [Deep Optimal Individualized Treatment Rules for Bivariate Survival Outcomes via Adaptive Prediction-Powered Learning](https://mlsanity.com/paper/2605.29464/) (2026)
- [Surv-IPTB: An Attention-Based Model for Estimating Individual Probability of Treatment Benefit with Survival Data](https://mlsanity.com/paper/2608.06288/) (2026)

## 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.