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Learning disentangled causal representations is a challenging problem thathas gained significant attention recently due to its implications forextracting meaningful information for downstream tasks. In this work, we definea new notion of causal disentanglement from the perspective of independentcausal mechanisms. We propose ICM-VAE, a framework for learning causallydisentangled representations supervised by causally related observed labels. Wemodel causal mechanisms using learnable flow-based diffeomorphic functions tomap noise variables to latent causal variables. Further, to promote thedisentanglement of causal factors, we propose a causal disentanglement priorthat utilizes the known causal structure to encourage learning a causallyfactorized distribution in the latent space. Under relatively mild conditions,we provide theoretical results showing the identifiability of causal factorsand mechanisms up to permutation and elementwise reparameterization. Weempirically demonstrate that our framework induces highly disentangled causalfactors, improves interventional robustness, and is compatible withcounterfactual generation.

Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms