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[2602.12373] Policy4OOD: A Knowledge-Guided World Model for Policy Intervention Simulation against the Opioid Overdose Crisis
Summary
The paper presents Policy4OOD, a knowledge-guided world model designed to simulate policy interventions against the opioid overdose crisis, enhancing decision-making through advanced forecasting and optimization techniques.
Why It Matters
This research addresses the critical challenge of evaluating public health policies in the context of the opioid epidemic, which is a pressing issue in the U.S. The model's ability to forecast outcomes and analyze counterfactual scenarios could significantly improve policy effectiveness and public health responses.
Key Takeaways
- Policy4OOD integrates forecasting, counterfactual reasoning, and optimization for effective policy evaluation.
- The model utilizes a policy-conditioned Transformer to enhance forecasting accuracy by incorporating spatial and socioeconomic data.
- A state-level dataset from 2019-2024 supports the model, providing insights into opioid mortality and policy impacts.
Computer Science > Machine Learning arXiv:2602.12373 (cs) [Submitted on 12 Feb 2026] Title:Policy4OOD: A Knowledge-Guided World Model for Policy Intervention Simulation against the Opioid Overdose Crisis Authors:Yijun Ma, Zehong Wang, Weixiang Sun, Zheyuan Zhang, Kaiwen Shi, Nitesh Chawla, Yanfang Ye View a PDF of the paper titled Policy4OOD: A Knowledge-Guided World Model for Policy Intervention Simulation against the Opioid Overdose Crisis, by Yijun Ma and 6 other authors View PDF HTML (experimental) Abstract: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 this http URL trained, the world model ...