Graph Neural Networks for Modeling Causality in International Trade

Abstract

Neural network algorithms have proven successful for accurate classifications in many domains such as image recognition and semantic parsing. However, they have long suffered from the lack of ability to measure causality, predict outliers effectively, or provide explainability relevant to the application domain. In this paper we introduce a method that measures causal scenarios during outlier events using neural networks: Artificial Intelligence Network Explanation of Trade (AINET). AINET tailors AI techniques specifically for bilateral trade modeling. Datasets with network-like structures (such as global trade, social networks, or city traffic) can benefit from Graph Neural Networks (GNNs) modeling and structural power. These network-based models (i.e. GNNs) empower policy makers with an understanding of the fast-paced shifts in trade flows around the world due to outlier events such as increased tariffs, natural disasters, embargoes, pandemics, or trade wars. Our work is at the intersection of GNNs' optimization, causality, and their proper application to trade. AINET results are presented with an overall test mean absolute percentage error (MAPE) of 28%, demonstrating the efficacy and potential of harnessing this method.