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Causal structures for observational survival data provide crucial informationregarding the relationships between covariates and time-to-event. We derivemotivation from the information theoretic source coding argument, and show thatincorporating the knowledge of the directed acyclic graph (DAG) can bebeneficial if suitable source encoders are employed. As a possible sourceencoder in this context, we derive a variational inference based conditionalvariational autoencoder for causal structured survival prediction, which werefer to as DAGSurv. We illustrate the performance of DAGSurv on low andhigh-dimensional synthetic datasets, and real-world datasets such as METABRICand GBSG. We demonstrate that the proposed method outperforms other survivalanalysis baselines such as Cox Proportional Hazards, DeepSurv and Deephit,which are oblivious to the underlying causal relationship between dataentities.

DAGSurv: Directed Acyclic Graph Based Survival Analysis Using Deep Neural Networks