  Recent advances in causal inference techniques, more specifically, in the
theory of structural causal models, provide the framework for identification of
causal effects from observational data in the cases where the causal graph is
identifiable, i.e., the data generating mechanism can be recovered from the
joint distribution. However, no such studies have been done to demonstrate this
concept with a clinical example. We present a complete framework to estimate
the causal effect from observational data by augmenting expert knowledge in the
model development phase and with a practical clinical application. Our clinical
application entails a timely and important research question, i.e., the effect
of oxygen therapy intervention in the intensive care unit (ICU); the result of
this project is useful in a variety of disease conditions, including severe
acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We
used data from the MIMIC III database, a standard database in the machine
learning community that contains 58,976 admissions from an ICU in Boston, MA,
for estimating the oxygen therapy effect on morality. We also identified the
covariate-specific effect to oxygen therapy from the model for more
personalized intervention.
