Reward-Guided Survival Data Generation

The sharing of tabular survival data is restricted by privacy regulations. While synthetic data generation offers a promising alternative, existing methods frequently fail to simultaneously preserve long-term survival dynamics and patient privacy. We introduce SurvTabGen, a novel generative framework tailored specifically for survival data synthesis. Unlike prior multi-stage approaches, SurvTabGen jointly models covariates, event indicators, and survival time within a unified structurally aware generator. We first extract causal relationships among covariates via causal discovery. The generation then follows a dependency-aware sequence: event outcomes are conditioned on covariates, and survival times are conditioned on both covariates and events. Crucially, the generator is trained with a reinforcement learning-based survival-aware objective. By using the Integrated Absolute Difference (IAD) between real and synthetic survival curves as a reward signal, the model directly optimizes for macroscopic temporal fidelity. Extensive evaluations against 10 SOTA baselines across seven diverse real-world datasets demonstrate the superiority of SurvTabGen. Our results demonstrate that SurvTabGen provides a highly effective solution for generating high-fidelity, privacy-preserving synthetic cohorts that excel in survival preservation and data quality.