  In medicine, researchers often seek to infer the effects of a given treatment
on patients' outcomes. However, the standard methods for causal survival
analysis make simplistic assumptions about the data-generating process and
cannot capture complex interactions among patient covariates. We introduce the
Dynamic Survival Transformer (DynST), a deep survival model that trains on
electronic health records (EHRs). Unlike previous transformers used in survival
analysis, DynST can make use of time-varying information to predict evolving
survival probabilities. We derive a semi-synthetic EHR dataset from MIMIC-III
to show that DynST can accurately estimate the causal effect of a treatment
intervention on restricted mean survival time (RMST). We demonstrate that DynST
achieves better predictive and causal estimation than two alternative models.
