# An Interventional Approach to Real-Time Disaster Assessment via Causal Attribution

arXiv: https://arxiv.org/abs/2509.11676 · HTML: https://mlsanity.com/paper/2509.11676/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [Algorithmic Recourse: from Counterfactual Explanations to Interventions](https://mlsanity.com/paper/2002.06278/) (2020)
- [Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End](https://mlsanity.com/paper/2011.04917/) (2020)

## Built on by (most cited first, 1 of 1)

- [Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence](https://mlsanity.com/paper/2605.11348/) (2026)

## Abstract (arXiv)

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.