Before revision:  To tackle this sequential treatment effect estimation problem, we have developed a causal dynamic survival model (CDSM) that uses the potential outcomes framework with the Bayesian recurrent sub-networks  to estimate the difference in survival curves  . Using simulated survival datasets, CDSM has shown good causal effect estimation performance across scenarios of sample dimension, event rate, confounding and overlapping.
After revision:  To tackle this sequential treatment effect estimation problem, we have developed a causal dynamic survival model (CDSM) that uses the potential outcomes framework with the Bayesian recurrent sub-networks with random seed ensembles to estimate the difference in survival curves . Using simulated survival datasets, CDSM has shown good causal effect estimation performance across scenarios of sample dimension, event rate, confounding and overlapping.