George Chen, Shahriar Noroozizadeh, Jeremy Weiss, Xiaobin Shen
We lifted 4 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Shahriarnz14/SurvHTE-Bench | — | 3 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| HypothesisNetwork | Ran | Shahriarnz14/SurvHTE-Bench/models_causal_survival/survite_pytorch.py pointer only (licence: NONE) · get_code("4b690b06cc8a5006") |
| IPMUtils | Ran | Shahriarnz14/SurvHTE-Bench/models_causal_survival/survite_pytorch.py pointer only (licence: NONE) · get_code("fc8e1ec82494fb38") |
| RepresentationNetwork | Ran | Shahriarnz14/SurvHTE-Bench/models_causal_survival/survite_pytorch.py pointer only (licence: NONE) · get_code("8359bdb2ac03144d") |
| SurvITE | Not yet run | Shahriarnz14/SurvHTE-Bench/models_causal_survival/survite_pytorch.py pointer only (licence: NONE) · get_code("cae2edcec15eef35") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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. The benchmark spans (i) a modular suite of synthetic datasets with known ground truth, systematically varying causal assumptions and survival dynamics, (ii) semisynthetic datasets that pair real-world covariates with simulated treatments and outcomes, and (iii) real-world datasets from a twin study (with known ground truth) and from an HIV clinical trial. Across synthetic, semi-synthetic, and realworld settings, we provide the first rigorous comparison of survival HTE methods under diverse conditions and realistic assumption violations. SURVHTE-BENCH establishes a foundation for fair, reproducible, and extensible evaluation of causal survival methods. The data and code of our benchmark are available at: https://github.com/Shahriarnz14/SurvHTE-Bench.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2603.05483")
get_code_for_paper("2603.05483")
have("2603.05483")
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