  In this paper, we derive an explicit upper bound for the Wasserstein distance
between a functional of point processes and a Gaussian distribution. Using
Stein's method in conjunction with Malliavin's calculus and the Poisson
embedding representation, our result applies to a variety of point processes
including discrete and continuous Hawkes processes. In particular, we establish
an explicit convergence rate for stable continuous non-linear Hawkes processes
and for discrete Hawkes processes. Finally, we obtain an upper bound in the
context of nearly unstable Hawkes processes.
