摘 要
随着数字经济的快速发展，财税大数据已成为政府决策和政策评估的重要资源。本研究旨在构建基于财税大数据的政策效应预测模型，以提升政策制定的科学性和精准性。通过整合多源财税数据，采用机器学习与统计分析相结合的方法，模型能够动态捕捉政策实施过程中的关键变量及其相互作用机制。研究创新性地引入了时间序列分解技术与因果推断框架，有效解决了传统方法在复杂政策环境下的适应性不足问题。实验结果表明，该模型在预测精度和解释能力上均显著优于现有方法，特别是在税收优惠、财政补贴等具体政策场景中表现出优异的适用性。此外，模型还具备较强的可扩展性，能够为不同层级政府提供定制化的政策模拟与优化建议。本研究的主要贡献在于，不仅为财税领域政策效应评估提供了新工具，还推动了大数据技术在公共管理领域的深度应用，为实现数据驱动型治理模式奠定了理论与实践基础。
关键词：财税大数据；政策效应预测；机器学习；因果推断；时间序列分解
A Policy Effect Prediction Model Driven by Tax and Financial Big Data
Abstract: With the rapid development of the digital economy, tax and fiscal big data have become crucial resources for government decision-making and policy evaluation. This study aims to construct a policy effect prediction model based on tax and fiscal big data to enhance the scientificity and precision of policy formulation. By integrating multi-source tax and fiscal data and employing a combination of machine learning and statistical analysis, the model is capable of dynamically capturing key variables during the policy implementation process and their interaction mechanisms. Innovatively, this research introduces time-series decomposition techniques and a causal inference fr amework, effectively addressing the adaptability limitations of traditional methods in complex policy environments. Experimental results demonstrate that the proposed model significantly outperforms existing approaches in terms of predictive accuracy and interpretability, particularly in specific policy scenarios such as tax incentives and fiscal subsidies. Additionally, the model exhibits strong scalability, enabling customized policy simulation and optimization recommendations for governments at various levels. The primary contribution of this study lies not only in providing a new tool for assessing policy effects in the tax and fiscal domain but also in advancing the deep application of big data technology in public management, thereby laying a theoretical and practical foundation for achieving data-driven governance.
Keywords: Tax And Financial Big Data; Policy Effect Prediction; Machine Learning; Causal Inference; Time Series Decomposition
目 录
一、绪论 1
（一）财税大数据与政策效应预测的研究背景 1
（二）国内外研究现状分析 1
（三）本文研究方法与技术路线 1
二、财税大数据驱动的理论基础与框架构建 2
（一）财税大数据的核心特征与价值 2
（二）政策效应预测模型的理论支撑 2
（三）数据驱动型预测框架的设计 3
三、财税大数据采集与预处理技术 4
（一）大数据采集的关键技术与挑战 4
（二）数据清洗与质量控制方法 4
（三）数据标准化与整合策略 5
四、政策效应预测模型的设计与实现 5
（一）预测模型的算法选择与优化 5
（二）模型参数调优与验证方法 6
（三）实证案例分析与结果评估 6
结论 7
参考文献 8
致 谢 9
