Global Tariff Shocks and U.S. Agriculture: Causal Machine Learning Approaches to Competitiveness and Market Share Forecasting

Global tariff fluctuations present significant challenges to the competitiveness and sustainability of U.S. agricultural exports.Historically, changes in tariff structures-whether from retaliatory actions, trade renegotiations, or shifts in geopolitical alliances-have induced volatile shifts in global market shares, farm incomes, and supply chain stability.Traditional econometric models, while valuable for trend analysis, often struggle to isolate causal relationships and predict nuanced competitive dynamics under rapidly evolving policy environments.This study explores the application of causal machine learning (CML) techniques, including causal forests, synthetic control methods, and targeted maximum likelihood estimation, to forecast the impacts of global tariff shocks on U.S. agricultural competitiveness.By leveraging high-frequency trade data, tariff schedules, and market intelligence indicators, we develop predictive models that move beyond correlation, focusing instead on estimating counterfactual scenarios and heterogeneous treatment effects across commodity classes.The research covers major U.S. export commodities such as soybeans, corn, and dairy products, illustrating how CML approaches enhance foresight into market share erosion or resilience under different tariff regimes.Particular attention is given to differentiating impacts across trading partners, regional markets, and commodity types.The findings highlight the strengths of causal machine learning in enabling policymakers and agribusiness leaders to anticipate strategic vulnerabilities, optimize export strategies, and design more resilient agricultural trade frameworks.This study positions CML as an essential toolkit for navigating the increasingly complex intersections of international trade policy and agricultural economics.