机器学习在规模性返贫监测预警与因果分析中的应用研究

Research on Machine Learning in Monitoring,Warning,and Causal Analysis of Large-scale Poverty-returning

坚决守住不发生规模性返贫底线是全面推进乡村振兴的前提。规模性返贫作为复杂多元的致贫风险冲击不同脆弱群体的结果具有突发性、区域性和群体性,传统方法难以有效对之进行度量、监测与预警。目前学术界对于规模性返贫的研究大多停留在理论层面,而机器学习的引入为规模性返贫的识别、预测与实证分析提供了新的思路与方法。本文梳理了机器学习在规模性返贫监测预警与因果分析中的研究进展,探讨了机器学习在规模性返贫预测与研究中的应用前景,为我国过渡期规模性返贫风险量度、监测预警及实证研究提供可选的方法与工具。

Firmly holding the bottom line of preventing large-scale poverty-returning is a prerequisite for comprehensively promoting rural revitalization.Large-scale poverty-returning,as a result of complex and diverse poverty risk impacts on different vulnerable groups,has characteristics of suddenness,regionalism,and collectivity.So traditional methods are difficult to effectively measure,monitor,and warn against it.At present,most of the researches on large-scale poverty-returning in the academic community remain at the theoretical level.But the introduction of machine learning provides new ideas and methods for the identification,prediction,and empirical analysis of large-scale poverty-returning.This paper reviews the research progress of machine learning in the monitoring,warning,and causal analysis of large-scale poverty-returning,explores the application prospects of machine learning in the prediction and research of large-scale poverty-returning.We hope to provide optional methods and tools for measuring,monitoring,warning,and empirical research on the risk of large-scale poverty-returning during the transitional period in China.