  Identifying the causal relationships between subjects or variables remains an
important problem across various scientific fields. This is particularly
important but challenging in complex systems, such as those involving human
behavior, sociotechnical contexts, and natural ecosystems. By exploiting state
space reconstruction via lagged embedding of time series, convergent cross
mapping (CCM) serves as an important method for addressing this problem. While
powerful, CCM is computationally costly; moreover, CCM results are highly
sensitive to several parameter values. While best practice entails exploring a
range of parameter settings when assessing casual relationships, the resulting
computational burden can raise barriers to practical use, especially for long
time series exhibiting weak causal linkages. We demonstrate here several means
of accelerating CCM by harnessing the distributed Apache Spark platform. We
characterize and report on results of several experiments with parallelized
solutions that demonstrate high scalability and a capacity for over an order of
magnitude performance improvement for the baseline configuration. Such
economies in computation time can speed learning and robust identification of
causal drivers in complex systems.
