Weakly supervised causal representation learning

Johann Brehmer, Pim De Haan, Phillip Lippe, Taco Cohen

Keywords: causal representation learning, causality, disentangled representation learning

TL;DR: We show that causal factors and their causal structure can be identified from low-level data (e.g. pixels) observed before and after interventions.

Abstract: Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from observational data alone. We prove under mild assumptions that this representation is identifiable in a weakly supervised setting. This requires a dataset with paired samples before and after random, unknown interventions, but no further labels. Finally, we show that we can infer the representation and causal graph reliably in a simple synthetic domain using a variational autoencoder with a structural causal model as prior.