An Analytical Framework for Causal Decision-Making in International Trade

Data for the paper "An Analytical Framework for Causal Decision-Making in International Trade"<br><b>Abstract</b>:<br><br>In international trade, selecting an Incoterm is a critical decision that directly impacts a firm's costs and risks. Previous quantitative studies have relied on traditional econometric models with restrictive assumptions, limiting their ability to provide causal insights for decision-making. This paper addresses this gap by developing and applying a causal inference framework to empirically evaluate the key determinants of this choice for Colombian exporting firms. Our methodology integrates directed acyclic graphs to model causal assumptions, uses nested cross-validated random forests to capture nonlinear relationships, and employs the G-computation formula to estimate robust causal effects. Our results indicate that the selection of Incoterms is primarily driven by transactional characteristics, specifically cargo type, shipment weight, and value. For example, the transition from bulk to containerized cargo is associated with a 13.4 percentage-point increase in the probability of opting for an E/F group term (e.g., FOB or FCA).. We also identify nonlinear effects for shipment weight and value, where E/F terms become ignificantly more probable only after a shipment surpasses a certain size threshold a pattern missed by prior linear models. Relational factors are also influential; sharing a common language with a trade partners has a significant causal effect, increasing the probability of using E/F terms by 6.5 percentage points. This research provides a data-driven framework for strategic Incoterm selection, enabling managers to align their choices with specific shipment characteristics and offering practical recommendations for logistics providers and policymakers.<br>