Variational autoencoders (VAEs) are generative models, akin to generative adversarial networks. Their association with this group of models derives mainly from the architectural affinity with the basic autoencoder (the final training objective has an encoder and a decoder), but their mathematical formulation differs significantly. VAEs are directed probabilistic graphical models (DPGM) whose posterior is approximated by a neural network, forming an autoencoder-like architecture. Unlike discriminative modeling that aims to learn a predictor given observation, "generative modeling" tries to learn how the data is generated, and to reflect the underlying causal relations. Causal relations have the potential for generalizability.