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Workshop: Workshop on the Elements of Reasoning: Objects, Structure and Causality

Weakly supervised causal representation learning

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


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.

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