We present jumping, a novel form of selective control-flow abstraction useful for improving the scalability of goal-directed static analyses. We motivate the utility of jumping in the context of analyzing event-driven systems. In such systems, accounting for orderings between certain events is important for precision, yet analyzing the product graph of all possible event orderings is intractable. Jumping solves this problem by allowing the analysis to selectively abstract away control-flow between events irrelevant to a goal query while preserving information about the ordering of relevant events. We present a framework for designing sound jumping analyses and create an instantiation of the framework for performing precise inter-event analysis of Android applications. Our experimental evaluation showed that using jumping to augment a precise goal-directed analysis with inter-event reasoning enables our analysis to prove 90–97% of dereferences safe across our benchmarks.