用知识引导的世界模型模拟药物过量政策效果，预测、反事实分析和优化一气呵成。
Policy4OOD: A Knowledge-Guided World Model for Policy Intervention Simulation against the Opioid Overdose Crisis
- 构建融合政策图谱与时空数据的Transformer模型，统一预测与干预评估
- 在2019–2024年州级月度数据上，空间依赖与结构化政策知识提升预测准确率
- 支持前向预测、反事实推演与策略优化，适合公共健康决策者使用
阿片类药物危机是美国最严重的公共卫生问题之一，但政策干预前的评估极为困难：多项政策在动态系统中相互作用，针对某一风险路径可能意外加剧另一路径。我们提出有效政策评估需具备三大能力——基于当前政策预测未来结果、对历史决策进行反事实推理、在候选干预中进行优化——并主张通过世界建模统一实现。为此，我们提出Policy4OOD，一种知识引导的时空世界模型，解决三个核心挑战：政策应如何制定、影响在何处显现、效应何时发生。该模型联合编码政策知识图谱、州级空间依赖与经济社会时间序列，通过政策条件化的Transformer预测未来阿片类药物结局。训练完成后，世界模型可作为模拟器：预测只需前向传播，反事实分析替换历史序列中的政策编码，策略优化则在学习到的模拟器上使用蒙特卡洛树搜索。为支持该框架，我们构建了一个2019–2024年州级月度数据集，整合阿片类药物死亡率、经济社会指标与结构化政策编码。实验表明，空间依赖与结构化政策知识显著提升预测精度，验证了各组件的有效性及世界建模在数据驱动公共卫生决策中的潜力。代码与数据已公开于https://github.com/antman9914/Policy4OOD。
原文摘要 · Abstract (English)
The opioid epidemic remains one of the most severe public health crises in the United States, yet evaluating policy interventions before implementation is difficult: multiple policies interact within a dynamic system where targeting one risk pathway may inadvertently amplify another. We argue that effective opioid policy evaluation requires three capabilities -- forecasting future outcomes under current policies, counterfactual reasoning about alternative past decisions, and optimization over candidate interventions -- and propose to unify them through world modeling. We introduce Policy4OOD, a knowledge-guided spatio-temporal world model that addresses three core challenges: what policies prescribe, where effects manifest, and when effects unfold.Policy4OOD jointly encodes policy knowledge graphs, state-level spatial dependencies, and socioeconomic time series into a policy-conditioned Transformer that forecasts future opioid outcomes.Once trained, the world model serves as a simulator: forecasting requires only a forward pass, counterfactual analysis substitutes alternative policy encodings in the historical sequence, and policy optimization employs Monte Carlo Tree Search over the learned simulator. To support this framework, we construct a state-level monthly dataset (2019--2024) integrating opioid mortality, socioeconomic indicators, and structured policy encodings. Experiments demonstrate that spatial dependencies and structured policy knowledge significantly improve forecasting accuracy, validating each architectural component and the potential of world modeling for data-driven public health decision support. Our code and data have been released in https://github.com/antman9914/Policy4OOD.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。