Cause and Effect: Hierarchical Concept-based Explanation of Neural Networks

In many scenarios, human decisions are explained based on some high-level concepts. In this work, we take a step in the interpretability of neural networks by examining their internal representation or neuron's activations against concepts. A concept is characterized by a set of samples that have specific features in common. We propose a framework to check the existence of a causal relationship between a concept (or its negation) and task classes. While the previous methods focus on the importance of a concept to a task class, we go further and introduce four measures to quantitatively determine the order of causality. Moreover, we propose a method for constructing a hierarchy of concepts in the form of a concept-based decision tree which can shed light on how various concepts interact inside a neural network towards predicting output classes. Through experiments, we demonstrate the effectiveness of the proposed method in explaining the causal relationship between a concept and the predictive behaviour of a neural network as well as determining the interactions between different concepts through constructing a concept hierarchy.