Incorporating Causal Effects into Deep Learning Predictions on EHR Data

TL;DR

This paper introduces a novel method to incorporate causal effects into deep learning models for EHR data, improving prediction accuracy and interpretability by addressing causal inference challenges and identifying causal information gaps.

Contribution

It proposes a generalized estimation vector for causal effects and demonstrates how integrating it enhances deep learning performance on EHR data.

Findings

Improved predictive accuracy in EHR analysis

Enhanced interpretability of deep learning models

Identification of causal information blink spots

Abstract

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.