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政策学习作为人工智能技术与因果推断理论的深度融合,为大数据时代的精准社会治理提供了科学依据。本文立足于中国治理规模巨大、政策约束条件复杂的现实背景,系统梳理了政策学习领域的国际前沿文献,并提出一种基于凸化处理理论的政策学习算法。该方法有效解决了在复杂高维政策空间中的优化计算难题,显著提升了大数据及多约束场景下的政策学习效率。文章进一步以中国最低生活保障制度为例,利用中国家庭金融调查数据进行实证分析。结果表明,该方法能够生成具备高度可解释性的精准低保分配方案,有效扩大社会福利增益并提升治理效能。本研究为运用前沿数智技术推动中国社会治理现代化提供了重要参考。
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.
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(1)出自习近平总书记2024年4月22日至24日在重庆考察时的讲话,参见:https://www.gov.cn/yaowen/liebiao/202404/content_6947266.htm.
(1)参见:https://www.ndrc.gov.cn/fggz/jyysr/jysrsbxf/202304/t20230428_1355158.html。
基本信息:
中图分类号:TP18;D632.1
引用信息:
[1]方悦,胡诗云,苏良军,等.数智时代的政策学习方法探索——以最低生活保障为例[J].经济学报,2026(02):55-72.
基金信息:
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);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
2026-07-29
2026-07-29
2026-07-29