Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness
This work addresses a foundational problem in machine learning for researchers and practitioners seeking robust and data-efficient neural networks, though it appears incremental as it builds on existing causal perspectives.
The paper tackles the lack of a commonly accepted definition and validation procedure for disentangled representations in neural networks by introducing a causal framework that covers disentanglement and domain shift robustness, and proposes a new metric for quantitative evaluation of deep latent variable models with an efficient linear-scaling estimation algorithm.
The ability to learn disentangled representations that split underlying sources of variation in high dimensional, unstructured data is important for data efficient and robust use of neural networks. While various approaches aiming towards this goal have been proposed in recent times, a commonly accepted definition and validation procedure is missing. We provide a causal perspective on representation learning which covers disentanglement and domain shift robustness as special cases. Our causal framework allows us to introduce a new metric for the quantitative evaluation of deep latent variable models. We show how this metric can be estimated from labeled observational data and further provide an efficient estimation algorithm that scales linearly in the dataset size.