经济学报2026，Vol.13Issue(2)：55-72,18.
数智时代的政策学习方法探索
Policy Learning in the Digital Age:A Case Study of Precise Policy-Making in the China's Minimum Livelihood Guarantee
摘要
Abstract
Policy learning,as a deep integration of artificial intelligence technologies with causal inference theory,provides a scientific foundation for precision social governance in the era of big data.Grounded in the practical context of China's large-scale governance and complex policy constraints,this paper systematically reviews the international frontier literature on policy learning and proposes a policy learning algorithm based on convexification theory.This approach effectively addresses the computational optimization challenges inherent in complex,high-dimensional policy spaces,thereby significantly improving policy learning efficiency under big data and multiple-constraint settings.The paper further conducts an empirical analysis using China's Minimum Livelihood Guarantee(Dibao)program as a case study,drawing on data from the China Household Finance Survey(CHFS).The results demonstrate that the proposed method can generate highly interpretable and precisely targeted Dibao allocation schemes,effectively expanding social welfare gains and enhancing governance efficiency.This study provides an important reference for leveraging cutting-edge digital and intelligent technologies to advance the modernization of social governance in China.关键词
数字治理/政策学习/因果推断/大数据方法/最低生活保障分类
社会科学引用本文复制引用
方悦,胡诗云,苏良军,解海天..数智时代的政策学习方法探索[J].经济学报,2026,13(2):55-72,18.基金项目
本研究受国家自然科学基金青年项目"政策学习中的理论创新与实践应用"(72503208)、国家自然科学基金重点项目"高维计量模型的机器学习方法及其在经济管理中的应用"(72133002)、国家自然科学基金青年项目"基于机器学习的因果政策制定——政策学习"(72403008)和重大项目"大规模商务场景下的统计学习与管理实践"(72495123)的资助. （72503208）
