An Interventional Approach to Real-Time Disaster Assessment via Causal Attribution
Abstract
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.
Keywords
Cite
@article{arxiv.2509.11676,
title = {An Interventional Approach to Real-Time Disaster Assessment via Causal Attribution},
author = {Saketh Vishnubhatla and Alimohammad Beigi and Rui Heng Foo and Umang Goel and Ujun Jeong and Bohan Jiang and Adrienne Raglin and Huan Liu},
journal= {arXiv preprint arXiv:2509.11676},
year = {2025}
}