  Survival outcomes are common in comparative effectiveness studies and require
unique handling because they are usually incompletely observed due to
right-censoring. A ``once for all'' approach for causal inference with survival
outcomes constructs pseudo-observations and allows standard methods such as
propensity score weighting to proceed as if the outcomes are completely
observed. For a general class of model-free causal estimands with survival
outcomes on user-specified target populations, we develop corresponding
propensity score weighting estimators based on the pseudo-observations and
establish their asymptotic properties. In particular, utilizing the functional
delta-method and the von Mises expansion, we derive a new closed-form variance
of the weighting estimator that takes into account the uncertainty due to both
pseudo-observation calculation and propensity score estimation. This allows
valid and computationally efficient inference without resampling. We also prove
the optimal efficiency property of the overlap weights within the class of
balancing weights for survival outcomes. The proposed methods are applicable to
both binary and multiple treatments. Extensive simulations are conducted to
explore the operating characteristics of the proposed method versus other
commonly used alternatives. We apply the proposed method to compare the causal
effects of three popular treatment approaches for prostate cancer patients.
