Structural Causal Model with Expert Augmented Knowledge to Estimate the Effect of Oxygen Therapy on Mortality in the ICU

TL;DR

This paper introduces a novel framework combining expert knowledge with structural causal models to estimate the causal effect of oxygen therapy on ICU mortality using observational data, demonstrating its clinical relevance.

Contribution

It presents a complete methodology integrating expert knowledge into causal models for clinical effect estimation from observational data.

Findings

Estimated the causal effect of oxygen therapy on ICU mortality.

Identified covariate-specific effects for personalized treatment.

Validated the approach using MIMIC III ICU database.

Abstract

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.