Healthcare Analytics
SURVHTE-BENCH: A BENCHMARK FOR HETEROGENEOUS TREATMENT EFFECT ESTIMATION IN SURVIVAL ANALYSIS
Estimating heterogeneous treatment effects (HTEs) from right-censored survival data is critical in high-stakes applications such as precision medicine and individualized policy-making. Yet, the survival analysis setting poses unique challenges for HTE estimation due to censoring, unobserved counterfactuals, and complex identification assumptions. Despite recent advances, from Causal Survival Forests to survival meta-learners and outcome imputation approaches, evaluation practices remain fragmented and inconsistent. We introduce SURVHTE-BENCH, the first comprehensive benchmark for HTE estimation with censored outcomes.
Executive Impact: Key Metrics at a Glance
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Methods Benchmarked
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Synthetic Datasets
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Max Censoring Rate
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Experimental Repeats
Deep Analysis & Enterprise Applications
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Method Unification
Our benchmark introduces a unified framework for survival HTE methods, categorizing them into three families: outcome imputation, direct-survival causal methods, and survival meta-learners. We provide a modular implementation of 53 methods across these families, which is the first systematic framework to unify these approaches, facilitating reproducibility and extensibility. This structure allows for consistent evaluation across diverse datasets and estimands.
Enterprise Process Flow
Outcome Imputation Methods (42 Variants)
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Direct-Survival CATE Methods (2 Variants)
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Survival Meta-Learners (9 Variants)
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Unified Evaluation
Synthetic Benchmarking Design
We present a curated suite of 40 synthetic datasets, systematically varying across eight causal configurations and five survival scenarios. This design provides controlled settings with known ground-truth HTEs, allowing for rigorous evaluation under realistic assumption violations. The systematic variation covers randomization, unobserved confounding, overlap violation, informative censoring, and diverse survival and censoring distributions.
| Causal Configuration | Key Characteristics |
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| RCT-50 |
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| OBS-UConf-InfC |
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| OBS-NoPos-InfC |
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Semi-Synthetic Data Results
To bridge the gap between controlled synthetic experiments and real-world complexity, we include 10 semi-synthetic datasets. These datasets pair real covariates from sources like ACTG HIV trials and MIMIC-IV ICU records with simulated treatments and outcomes. This approach preserves realistic feature distributions and correlations while retaining ground-truth CATEs for rigorous evaluation under moderate to extreme censoring regimes and covariate-dependent assignments.
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Lowest RMSE achieved by Double-ML in ACTG semi-synthetic dataset, highlighting flexible doubly robust approaches in moderate-dimensional settings.
Real-World Data Evaluation
We incorporate two widely studied real datasets: the Twins dataset (with known ground truth) and the HIV clinical trial dataset (without known ground truth). The Twins dataset, derived from twin births, allows for direct CATE evaluation as one twin serves as a counterfactual for the other. The HIV clinical trial data from ACTG 175 is used to test robustness under artificially introduced high censoring rates, revealing how methods behave under real covariate and outcome structures.
Case Study: HIV Clinical Trial Data (ACTG 175)
The ACTG 175 dataset compared four antiretroviral treatments in 2,139 HIV-infected patients. Our evaluation involved analyzing CATE estimates between baseline and high-censoring conditions. We observed that Causal Survival Forests produced estimates that clustered tightly around their original values under increased censoring, while outcome imputation methods showed higher variation, and survival meta-learners exhibited substantial deviations, indicating their sensitivity to censoring conditions.
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