Partial Disentanglement via Mechanism Sparsity

Understand

Disentanglement via mechanism sparsity was introduced recently as a principled approach to extract latent factors without supervision when the causal graph relating them in time is sparse, and/or when actions are observed and affect them sparsely.

• However, this theory applies only to ground-truth graphs satisfying a specific criterion.
• In this work, we introduce a generalization of this theory which applies to any ground-truth graph and specifies qualitatively how disentangled the learned representation is expected to be, via a new equivalence relation over models we call consistency.
• This equivalence captures which factors are expected to remain entangled and which are not based on the specific form of the ground-truth graph.