  We study analytically the performance of a recently proposed algorithm for
learning the couplings of a random asymmetric kinetic Ising model from finite
length trajectories of the spin dynamics. Our analysis shows the importance of
the nontrivial equal time correlations between spins induced by the dynamics
for the speed of learning. These correlations become more important as the
spin's stochasticity is decreased. We also analyse the deviation of the
estimation error from asymptotic optimality.
