We present a method based on Belief Propagation to study a series of inference problems on discrete dynamical cascade models based on partial and/or noisy observations of the cascades. The problems include the identification of the source, the discovery of undetected infected nodes, prediction of features of the future evolution, and the inference of the supporting network.