This article addresses issues from applied stochastic analysis for solving parameter identification problems in interaction networks. Purely discontinuous Markov processes are the most appropriate choice for modelling these systems and it is of paramount importance to estimate the unknown characteristics of the model given the measured data. The model induces a Fokker–Planck–Kolmogorov equation along with moment equations, and achieving parametric identification based on direct solutions of these equations has remained elusive. We propose a novel approach which utilizes stochastic analysis of continuous-time Markov processes to lift the curse of dimensionality in parametric identification. We illustrate through a case study from ecological engineering.