  Electronic Health Records (EHR) data analysis plays a crucial role in
healthcare system quality. Because of its highly complex underlying causality
and limited observable nature, causal inference on EHR is quite challenging.
Deep Learning (DL) achieved great success among the advanced machine learning
methodologies. Nevertheless, it is still obstructed by the inappropriately
assumed causal conditions. This work proposed a novel method to quantify
clinically well-defined causal effects as a generalized estimation vector that
is simply utilizable for causal models. We incorporated it into DL models to
achieve better predictive performance and result interpretation. Furthermore,
we also proved the existence of causal information blink spots that regular DL
models cannot reach.
