# AI for Anticipatory Action: Moving Beyond Climate Forecasting

arXiv: https://arxiv.org/abs/2307.15727 · HTML: https://mlsanity.com/paper/2307.15727/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 71 references that are in the map.
- First posted on arXiv 28 Jul 2023.

## Builds on (most cited first, 20 of 20)

- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Causal inference using invariant prediction: identification and confidence intervals](https://mlsanity.com/paper/1501.01332/) (2015)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [External Validity: From Do-Calculus to Transportability Across Populations](https://mlsanity.com/paper/1503.01603/) (2015)
- [A review of machine learning applications in wildfire science and management](https://mlsanity.com/paper/2003.00646/) (2020)
- [Invariant Causal Prediction for Sequential Data](https://mlsanity.com/paper/1706.08058/) (2017)
- [Bounds on the conditional and average treatment effect with unobserved confounding factors](https://mlsanity.com/paper/1808.09521/) (2018)
- [Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping](https://mlsanity.com/paper/1510.00098/) (2015)
- [Flood Prediction Using Machine Learning Models: Literature Review](https://mlsanity.com/paper/1908.02781/) (2019)
- [A Unifying Causal Framework for Analyzing Dataset Shift-stable Learning Algorithms](https://mlsanity.com/paper/1905.11374/) (2019)
- [Simple rules for complex decisions](https://mlsanity.com/paper/1702.04690/) (2017)
- [A Deep Learning Approach for Population Estimation from Satellite Imagery](https://mlsanity.com/paper/1708.09086/) (2017)
- [Satellite imagery analysis for operational damage assessment in Emergency situations](https://mlsanity.com/paper/1803.00397/) (2018)
- [Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning](https://mlsanity.com/paper/1806.00894/) (2018)
- [NADBenchmarks -- a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters](https://mlsanity.com/paper/2212.10735/) (2022)
- [Predictive modeling of movements of refugees and internally displaced people: Towards a computational framework](https://mlsanity.com/paper/2201.08006/) (2022)
- [Integrated causal-predictive machine learning models for tropical cyclone epidemiology](https://mlsanity.com/paper/2010.11330/) (2020)
- [Potential for allocative harm in an environmental justice data tool](https://mlsanity.com/paper/2304.05603/) (2023)
- [The Mythical Swing Voter](https://mlsanity.com/paper/1406.7581/) (2014)
- [Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen](https://mlsanity.com/paper/1806.08819/) (2018)

## Abstract (arXiv)

Disaster response agencies have been shifting from a paradigm of climate forecasting towards one of anticipatory action: assessing not just what the climate will be, but how it will impact specific populations, thereby enabling proactive response and resource allocation. Machine learning models are becoming exceptionally powerful at climate forecasting, but methodological gaps remain in terms of facilitating anticipatory action. Here we provide an overview of anticipatory action, review relevant applications of machine learning, identify common challenges, and highlight areas where machine learning can uniquely contribute to advancing disaster response for populations most vulnerable to climate change.