用实时数据做灾害评估，能模拟‘如果’场景并找出关键影响因素。
An Interventional Approach to Real-Time Disaster Assessment via Causal Attribution
- 基于卫星、新闻、社交媒体等实时数据，支持用户修改输入模拟反事实场景。
- 可识别不同因素对灾害严重度的因果贡献，定位关键影响源。
- 提供可操作建议，助力灾后减损规划，适合应急决策者使用。
传统灾害评估工具基于历史观测数据进行预测，仅能说明当前状态如何导致严重度评分，但不具备因果干预能力。本文提出一种新型干预式工具，利用卫星影像、新闻报道和社交媒体等实时数据，使用户能够修改输入状态，模拟各类‘如果’情景。该工具不仅能分析不同因素在特定区域对灾害严重度的因果影响，还可生成可执行的应对建议，辅助制定减灾预案。代码已公开。
原文摘要 · Abstract (English)
Traditional disaster analysis and modelling tools for assessing the severity of a disaster are predictive in nature. Based on the past observational data, these tools prescribe how the current input state (e.g., environmental conditions, situation reports) results in a severity assessment. However, these systems are not meant to be interventional in the causal sense, where the user can modify the current input state to simulate counterfactual "what-if" scenarios. In this work, we provide an alternative interventional tool that complements traditional disaster modelling tools by leveraging real-time data sources like satellite imagery, news, and social media. Our tool also helps understand the causal attribution of different factors on the estimated severity, over any given region of interest. In addition, we provide actionable recourses that would enable easier mitigation planning. Our source code is publicly available.
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