Using an interpretable Machine Learning approach to study the drivers of International Migration
This work addresses the need for interpretable models to inform migration policies, but it is incremental as it applies existing interpretability techniques to a specific domain.
The paper tackled the problem of modeling international migration flows by proposing an artificial neural network (ANN) to predict migration and using Partial Dependence Plots (PDP) to interpret the effects of drivers, finding that the ANN is more efficient than traditional models and PDP provides additional insights beyond feature importance.
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.