  Globally increasing migration pressures call for new modelling approaches in
order to design effective policies. It is important to have not only efficient
models to predict migration flows but also to understand how specific
parameters influence these flows. In this paper, we propose an artificial
neural network (ANN) to model international migration. Moreover, we use a
technique for interpreting machine learning models, namely Partial Dependence
Plots (PDP), to show that one can well study the effects of drivers behind
international migration. We train and evaluate the model on a dataset
containing annual international bilateral migration from $1960$ to $2010$ from
$175$ origin countries to $33$ mainly OECD destinations, along with the main
determinants as identified in the migration literature. The experiments carried
out confirm that: 1) the ANN model is more efficient w.r.t. a traditional
model, and 2) using PDP we are able to gain additional insights on the specific
effects of the migration drivers. This approach provides much more information
than only using the feature importance information used in previous works.
