This paper introduces a general method for the exploration of equivalence classes in the input space of Transformer models.

We propose Causally reliable Concept Bottleneck Models (C2BMs), a class of concept-based architectures that enforce reasoning through a bottleneck of concepts structured according to a model of the real-world causal mechanisms.