Machine learning in agricultural and applied economics
从应用经济学家的视角介绍机器学习方法，指出计量与模拟模型的局限，并探讨机器学习在预测、因果分析和复杂模拟中的潜在解决方案。
Abstract This review presents machine learning (ML) approaches from an applied economist’s perspective. We first introduce the key ML methods drawing connections to econometric practice. We then identify current limitations of the econometric and simulation model toolbox in applied economics and explore potential solutions afforded by ML. We dive into cases such as inflexible functional forms, unstructured data sources and large numbers of explanatory variables in both prediction and causal analysis, and highlight the challenges of complex simulation models. Finally, we argue that economists have a vital role in addressing the shortcomings of ML when used for quantitative economic analysis.