Casual Inference using Deep Bayesian Dynamic Survival Model (CDS)

Causal inference in longitudinal observational health data often requires the accurate estimation of treatment effects on time-to-event outcomes in the presence of time-varying covariates. To tackle this sequential treatment effect estimation problem, we have developed a causal dynamic survival (CDS) model that uses the potential outcomes framework with the recurrent sub-networks with random seed ensembles to estimate the difference in survival curves of its confidence interval. Using simulated survival datasets, the CDS model has shown good causal effect estimation performance across scenarios of sample dimension, event rate, confounding and overlapping. However, increasing the sample size is not effective to alleviate the adverse impact from high level of confounding. In two large clinical cohort studies, our model identified the expected conditional average treatment effect and detected individual effect heterogeneity over time and patient subgroups. CDS provides individualised absolute treatment effect estimations to improve clinical decisions.