Parameter and latent variable identifiability in variational autoencoders have received considerable attention recently, due to their empirical success in learning joint probabilities of complex data and their representations. Concurrently, modeling using multiple environments has been suggested for robust causal reasoning. We uncover additional theoretical benefits of multiple environments in the form of a strong identifiability result for a variational autoencoder model with latent covariate shift. We propose a novel learning algorithm that combines empirical Bayes and variational autoencoders, designed for latent variable identifiability without compromising representative power, using multiple environments as a crucial technical and practical tool.