Using Causal Analysis for Conceptual Deep Learning Explanation

TL;DR

This paper introduces a causal analysis approach to interpret deep learning models in healthcare, associating hidden units with clinical concepts and translating them into understandable decision rules for radiologists.

Contribution

It combines causal inference and sparse logistic regression to identify influential units and constructs interpretable decision trees for clinical explanations.

Findings

Model provides explanations aligned with clinical knowledge

Identifies sparse, meaningful concept-unit associations

Produces interpretable decision rules for clinicians

Abstract

Model explainability is essential for the creation of trustworthy Machine Learning models in healthcare. An ideal explanation resembles the decision-making process of a domain expert and is expressed using concepts or terminology that is meaningful to the clinicians. To provide such an explanation, we first associate the hidden units of the classifier to clinically relevant concepts. We take advantage of radiology reports accompanying the chest X-ray images to define concepts. We discover sparse associations between concepts and hidden units using a linear sparse logistic regression. To ensure that the identified units truly influence the classifier's outcome, we adopt tools from Causal Inference literature and, more specifically, mediation analysis through counterfactual interventions. Finally, we construct a low-depth decision tree to translate all the discovered concepts into a straightforward decision rule, expressed to the radiologist. We evaluated our approach on a large chest x-ray dataset, where our model produces a global explanation consistent with clinical knowledge.