Detecting causal relationships in complex systems from the time series of the individual units is a pressing area of research that has attracted the interest of a broad community. As an open area of study, this entails the development of methodologies to unravel causal relationships that evolve over time, such as switching of leader-follower roles in animal groups. Here, we augment the information theoretic measure of transfer entropy to establish a fitness function suitable for optimal partitioning of time series data to robustly detect leadership switches in collective behavior. The fitness function computes the information outflow from any agent in the group and rewards large sample sizes while normalizing with respect to available information. Our results indicate that for information-rich interactions, leadership switches within a group can be detected over relatively short time durations, with more than 90% accuracy. On a real soccer dataset, instances of leadership counted using the proposed approach are interestingly correlated with ball possession.