  Observational causal inference is useful for decision making in medicine when
randomized clinical trials (RCT) are infeasible or non generalizable. However,
traditional approaches fail to deliver unconfounded causal conclusions in
practice. The rise of "doubly robust" non-parametric tools coupled with the
growth of deep learning for capturing rich representations of multimodal data,
offers a unique opportunity to develop and test such models for causal
inference on comprehensive electronic health records (EHR). In this paper, we
investigate causal modelling of an RCT-established null causal association: the
effect of antihypertensive use on incident cancer risk. We develop a dataset
for our observational study and a Transformer-based model, Targeted BEHRT
coupled with doubly robust estimation, we estimate average risk ratio (RR). We
compare our model to benchmark statistical and deep learning models for causal
inference in multiple experiments on semi-synthetic derivations of our dataset
with various types and intensities of confounding. In order to further test the
reliability of our approach, we test our model on situations of limited data.
We find that our model provides more accurate estimates of RR (least sum
absolute error from ground truth) compared to benchmarks for risk ratio
estimation on high-dimensional EHR across experiments. Finally, we apply our
model to investigate the original case study: antihypertensives' effect on
cancer and demonstrate that our model generally captures the validated null
association.
