Before revision:  To tackle this sequential treatment effect estimation problem, we developed causal dynamic survival model (CDSM) for causal inference with survival outcomes using longitudinal electronic health record (EHR). CDSM has impressive explanatory performance while maintaining the prediction capability of conventional binary neural network predictors. It borrows the strength from explanatory framework including the survival analysis and counterfactual framework and integrates them with the prediction power from a deep Bayesian recurrent neural network to extract implicit knowledge from EHR data .
After revision:  To tackle this sequential treatment effect estimation problem, we developed 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. However, we found increasing the sample size is not effective if the original data is highly confounded or with low level of overlapping .