Abstract: Artificial intelligence is reshaping the paradigm of scientific research, yet its application in agricultural economics remains insufficient. Therefore, a systematic discussion is needed on its implementation pathways, conflicts faced, and resolution strategies within agricultural economics. Artificial intelligence has evolved from the natural sciences into the fields of social sciences and economics, finding applications in microbehavioral analysis and policy simulation. However, the predictive ability emphasized by artificial intelligence conflicts with the causal identification orientation in agricultural economics, which is currently the main obstacle to the application of artificial intelligence in agricultural economics. Considering the uncertainty of agricultural natural reproduction and the complexity of China’s production system dominated by small-scale farmers, blindly applying generic models may lead to decision-making biases and risks. A feasible solution is to integrate artificial intelligence into causal inference frameworks, enabling it to assist in variable selection, instrument variable construction, and counterfactual prediction under high-dimensional data structures. In addition, researchers should shift from simple data processing to knowledge reliability evaluation when using artificial intelligence tools, and use theoretical knowledge and field research experience to correct algorithm limitations. The above ideas provide a methodological framework for agricultural economics in the intelligent era that balances predictive advantages and causal inference needs.

Key words: artificial intelligence, agricultural economics, research paradigm, predictive power, causal identification