Innovations, opinions, ideas, recommendations or tendencies emerge in a variety of social networks. They can either disappear quickly or propagate and create considerable impact on the network. Their disappearance also may spread from one node to another across the network creating cascading behavior. Cascading phenomena are usually analyzed by: (i) identifying the most influential nodes according to their features in the network, and (ii) targeting a minimum set of nodes that could maximize the spread of influence within the network. In this paper we study the degree of the predictability of hidden cascading effect/failure specially focused on cascading of node removals. Predictions are modelled based on an extensive set of network features including node and path features. We compare the prediction accuracies over 5 synthetically generated networks that imitate real social networks. The cascading removal phenomenon is imitated by three well-known influence maximization cascading models in addition to two variants of a novel cascading strategy which are designed more consistent with human intuition over cascading removals. The prediction is done for an individual iteration of the cascading models, with the ability to be generalized on the entire course of cascade and without any loss of generality.