Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Records
Journal:
arXiv
Published Date:
Jun 3, 2025
Abstract
The importance of clinical variables in the prognosis of the disease is
explained using statistical correlation or machine learning (ML). However, the
predictive importance of these variables may not represent their causal
relationships with diseases. This paper uses clinical variables from a heart
failure (HF) patient cohort to investigate the causal explainability of
important variables obtained in statistical and ML contexts. Due to inherent
regression modeling, popular causal discovery methods strictly assume that the
cause and effect variables are numerical and continuous. This paper proposes a
new computational framework to enable causal structure discovery (CSD) and
score the causal strength of mixed-type (categorical, numerical, binary)
clinical variables for binary disease outcomes. In HF classification, we
investigate the association between the importance rank order of three feature
types: correlated features, features important for ML predictions, and causal
features. Our results demonstrate that CSD modeling for nonlinear causal
relationships is more meaningful than its linear counterparts. Feature
importance obtained from nonlinear classifiers (e.g., gradient-boosting trees)
strongly correlates with the causal strength of variables without
differentiating cause and effect variables. Correlated variables can be causal
for HF, but they are rarely identified as effect variables. These results can
be used to add the causal explanation of variables important for ML-based
prediction modeling.
