Identifying Coarse-grained Independent Causal Mechanisms with Self-supervision

Among the most effective methods for uncovering high dimensional unstructured data's generating mechanisms are techniques based on disentangling and learning independent causal mechanisms. However, to identify the disentangled model, previous methods need additional observable variables or do not provide identifiability results. In contrast, this work aims to design an identifiable generative model that approximates the underlying mechanisms from observational data using only self-supervision. Specifically, the generative model uses a degenerate mixture prior to learn mechanisms that generate or transform data. We outline sufficient conditions for an identifiable generative model up to three types of transformations that preserve a coarse-grained disentanglement. Moreover, we propose a self-supervised training method based on these identifiability conditions. We validate our approach on MNIST, FashionMNIST, and Sprites datasets, showing that the proposed method identifies disentangled models - by visualization and evaluating the downstream predictive model's accuracy under environment shifts.