Inferring coupling structure of complex systems from time series data is a challenging problem in applied science. The reliability of statistical inferences requires construction of information-theoretic measures taking into account both direct and indirect influences, manifest as information flow between the components within the system. We present application of the optimal causation entropy (oCSE) principle to identify coupling structure by aggregative discovery for progressive removal algorithms based on the oCSE principle, from measured data.