Using Causal Analysis for Conceptual Deep Learning Explanation
This work addresses the need for trustworthy AI in healthcare by offering explanations that are meaningful to clinicians, though it is incremental as it builds on existing causal inference and concept-based methods.
The paper tackled the problem of providing interpretable explanations for deep learning models in healthcare by associating hidden units with clinically relevant concepts using radiology reports and causal analysis, resulting in a global explanation consistent with clinical knowledge on a large chest X-ray dataset.
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.