We propose an empirically stable and asymptotically efficient covariate-balancing approach to the problem of estimating survival causal effects in data with conditionally-independent censoring. This addresses a challenge often encountered in state-of-the-art nonparametric methods: the use of inverses of small estimated probabilities and the resulting amplification of estimation error. We validate our theoretical results in experiments on synthetic and semi-synthetic data.

翻译：我们提出了一种在条件独立删失数据中估计生存因果效应的经验稳定且渐近有效的协变量平衡方法。该方法解决了当前最先进非参数方法中常遇到的挑战：使用小估计概率的倒数及其导致的估计误差放大问题。我们通过合成数据和半合成数据的实验验证了理论结果。