This chapter proposes neuro-causal models, a novel neuro-symbolic model architecture that uses a synthesis of deep generative models and causal graphical models to automatically infer higher level symbolic information from lower level “raw features”, while also allowing for rich relationships among the symbolic variables. Neuro-causal models retain the flexibility of modern deep neural network architectures while simultaneously capturing statistical semantics such as identifiability and causality, which are important to discuss ideal, target representations and their tradeoffs. We consider a general setting for this problem: No assumptions are placed on these relationships, and the number of hidden variables, their state spaces, and their relationships are presumed unknown. The primary objective is to provide explicit conditions under which all of this can be recovered uniquely, and to develop practical algorithms for learning these representations from data.
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