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The ability to learn disentangled representations that split underlyingsources of variation in high dimensional, unstructured data is important fordata efficient and robust use of neural networks. While various approachesaiming towards this goal have been proposed in recent times, a commonlyaccepted definition and validation procedure is missing. We provide a causalperspective on representation learning which covers disentanglement and domainshift robustness as special cases. Our causal framework allows us to introducea new metric for the quantitative evaluation of deep latent variable models. Weshow how this metric can be estimated from labeled observational data andfurther provide an efficient estimation algorithm that scales linearly in thedataset size.

Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness