We address the task of domain generalization, where the goalis to train a predictive model such that it is able to generalizeto a new, previously unseen domain. We choose a generativeapproach within the framework of variational autoencodersand propose an unsupervised algorithm that is able to gen-eralize to new domains without supervision. We show thatour method is able to learn representations that disentangledomain-specific information from class-label specific infor-mation even in complex settings where domain structure isnot observed during training. Our interpretable method out-performs previously proposed generative algorithms for do-main generalization and achieves competitive performancecompared to state-of-the-art approaches, which rely on ob-serving domain-specific information during training, on thestandard domain generalization benchmark dataset PACS.Additionally, we proposed weak domain supervision whichcan further increase the performance of our algorithm in thePACS dataset.