DAGSurv: Directed Acyclic Graph Based Survival Analysis Using Deep Neural Networks
This addresses survival prediction in fields like healthcare by leveraging causal relationships, though it is incremental as it builds on existing deep learning methods with a novel integration of DAGs.
The paper tackled survival analysis by incorporating causal structures (DAGs) into deep neural networks, resulting in a method called DAGSurv that outperformed baselines like Cox Proportional Hazards, DeepSurv, and Deephit on synthetic and real-world datasets such as METABRIC and GBSG.
Causal structures for observational survival data provide crucial information regarding the relationships between covariates and time-to-event. We derive motivation from the information theoretic source coding argument, and show that incorporating the knowledge of the directed acyclic graph (DAG) can be beneficial if suitable source encoders are employed. As a possible source encoder in this context, we derive a variational inference based conditional variational autoencoder for causal structured survival prediction, which we refer to as DAGSurv. We illustrate the performance of DAGSurv on low and high-dimensional synthetic datasets, and real-world datasets such as METABRIC and GBSG. We demonstrate that the proposed method outperforms other survival analysis baselines such as Cox Proportional Hazards, DeepSurv and Deephit, which are oblivious to the underlying causal relationship between data entities.