Efforts to model viral cascades provide a vital view into how they form and spread. A range of methods, such as Multivariate Hawkes Processes or network inference algorithms, attempt to decompose cascades into constituent components via inference -- by constructing an underlying network of influence, or by generating direct pairwise influence measures between cascade participants. While these approaches provide detailed models of the generative mechanics underlying event sequences, their sophistication often comes at a steep computational cost that prevents them from being applied to large-scale datasets. This is particularly the case for Multivariate Hawkes Processes. In this work, we propose a novel, scalable method for generating individual-level influence measures across a set of cascades. Across real-world datasets, we demonstrate the alignment of this approach's calculations with the influence inferred by established methods, as well as the computational scalability of this method.