  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.
