App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 7

Policy miner: neurosymbolic differentiable causal attribution framework for unstructured text in public policy

Yang Yi
Li Chenxia
College of Artificial Intelligence, Chongqing Technology and Business University, Chongqing 400067, China
DOI: 10.19734/j.issn.1001-3695.2025.11.0452

Abstract

To address the complexity, multidimensionality, and causal heterogeneity of public policy provisions, this study proposes Policy-Miner, a neuro-symbolic differentiable causal attribution framework for end-to-end automated analysis from unstructured policy texts to causal effect identification. The framework compiled policy clauses into quantifiable Differentiable Policy Rules (DPRs) using differentiable logic. It also integrated causal representation learning with graph-constrained Shapley value decomposition to identify heterogeneous treatment groups and characterize inter-clause interaction effects. Empirical analysis of the 2017 U. S. Tax Cuts and Jobs Act (TCJA) demonstrated that Policy-Miner significantly outperforms mainstream statistical methods and pure representation learning baselines in causal resolution. Ablation experiments verified that incorporating differentiable logic improves logical attribution stability from -0.13 to 1.00, effectively overcoming the boundary noise sensitivity of traditional hard logic. Results indicate that the framework achieves strong robustness and precision in complex policy attribution tasks, offering a scalable computational tool for automated public policy evaluation and causal analysis.

Foundation Support

重庆市科学技术局面上(一般)项目(CSTB2025NSCQ-GPX0133)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.11.0452
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 7

Publish History

[2026-03-18] Accepted Paper

Cite This Article

杨艺, 李晨霞. Policy-Miner：面向公共政策非结构化文本的神经符号可微分因果归因框架 [J]. 计算机应用研究, 2026, 43 (7). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0452. (Yang Yi, Li Chenxia. Policy miner: neurosymbolic differentiable causal attribution framework for unstructured text in public policy [J]. Application Research of Computers, 2026, 43 (7). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0452. )

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  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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