Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU

Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the joint distribution. However, no such studies have been performed to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effects 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 essential research question, the effect of oxygen therapy intervention in the intensive care unit (ICU). The result of this project is helpful 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 widely used health care database in the machine learning community with 58,976 admissions from an ICU in Boston, MA, to estimate the oxygen therapy effect on morality. We also identified the model’s covariate-specific effect on oxygen therapy for more personalized intervention. •Causal inference can help estimate causal effects, given the causal model is known.•Using Causal Inference, we aim to find the causal effect of oxygen therapy at ICU.•We leveraged observational data and expert knowledge to find underlying causal model.•We extracted cohort data from MIMIC-III database, a large public healthcare dataset.•Proposed causal method is suitable for exploring a broader set of clinical questions.