An Interventional Approach to Real-Time Disaster Assessment via Causal Attribution
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arXiv
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| Format: | Preprint |
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2025
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| author | Vishnubhatla, Saketh Beigi, Alimohammad Foo, Rui Heng Goel, Umang Jeong, Ujun Jiang, Bohan Raglin, Adrienne Liu, Huan |
| author_facet | Vishnubhatla, Saketh Beigi, Alimohammad Foo, Rui Heng Goel, Umang Jeong, Ujun Jiang, Bohan Raglin, Adrienne Liu, Huan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11676 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Interventional Approach to Real-Time Disaster Assessment via Causal Attribution Vishnubhatla, Saketh Beigi, Alimohammad Foo, Rui Heng Goel, Umang Jeong, Ujun Jiang, Bohan Raglin, Adrienne Liu, Huan Machine Learning 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. |
| title | An Interventional Approach to Real-Time Disaster Assessment via Causal Attribution |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.11676 |