  This paper presents a novel methodology for predicting international
bilateral trade flows, emphasizing the growing importance of Preferential Trade
Agreements (PTAs) in the global trade landscape. Acknowledging the limitations
of traditional models like the Gravity Model of Trade, this study introduces a
two-stage approach combining explainable machine learning and factorization
models. The first stage employs SHAP Explainer for effective variable
selection, identifying key provisions in PTAs, while the second stage utilizes
Factorization Machine models to analyze the pairwise interaction effects of
these provisions on trade flows. By analyzing comprehensive datasets, the paper
demonstrates the efficacy of this approach. The findings not only enhance the
predictive accuracy of trade flow models but also offer deeper insights into
the complex dynamics of international trade, influenced by specific bilateral
trade provisions.
