Decomposing decisions: causal concept explanations for deep learning models
TL;DR
The Causal Concept Decomposer (CCD) is introduced, a three-stage framework for concept-driven causal explanation of object detectors that produces explanations that are both visually coherent and quantitatively more faithful than existing approaches.
Abstract
ABSTRACT The deployment of deep learning models in high-stakes applications such as autonomous driving is critically important, yet their black-box nature remains a fundamental barrier to trust and accountability. Existing explainability methods typically produce ambiguous, pixel-based heatmaps that capture correlation rather than establishing a causal link between high-level, human-interpretable concepts and model outputs. This paper introduces the Causal Concept Decomposer (CCD), a three-stage framework for concept-driven causal explanation of object detectors. CCD first employs semantic segmentation to isolate the target object, then applies Non-negative Matrix Factorization to discover constituent semantic parts, and finally uses Sobol sensitivity analysis to quantify the causal influence of each part on the detector’s decision. Evaluated on the MS COCO dataset, CCD produces explanations that are both visually coherent and quantitatively more faithful than existing approaches, achieving a Deletion AUC of 0.11 and an Insertion AUC of 0.91. By moving beyond correlational attribution towards principled causal analysis, this work represents an important step towards more trustworthy and reliable AI systems.