Abstract: Survival outcomes are common in comparative effectiveness studies and require unique handling, because they are usually incompletely observed owing to right-censoring. A "once for all" approach for causal inference with survival out- comes 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 such pseudo-observations and establish their asymptotic properties. In particular, using 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 the pseudo-observation calculation and the propensity score estimation. This allows for a 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 com- pare the causal effects of three popular treatment approaches for prostate cancer patients.

Key words and phrases: Balancing weights, causal inference, multiple treatments, overlap weights, survival analysis.