# Climate Variability Modulates the Impact of Price Spikes on Food Insecurity

arXiv: https://arxiv.org/abs/2609.24394 · HTML: https://mlsanity.com/paper/2609.24394/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 57 references that are in the map.
- First posted on arXiv 21 Sep 2026.

## Builds on (most cited first, 6 of 6)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Selective machine learning of doubly robust functionals](https://mlsanity.com/paper/1911.02029/) (2019)
- [Forecasting trends in food security with real time data](https://mlsanity.com/paper/2312.00626/) (2023)
- [A monthly sub-national Harmonized Food Insecurity Dataset for comprehensive analysis and predictive modeling](https://mlsanity.com/paper/2501.06076/) (2025)
- [Learning Causal Response Representations through Direct Effect Analysis](https://mlsanity.com/paper/2503.04358/) (2025)

## Abstract (arXiv)

Climate variability influences whether a market disruption escalates into a food crisis, yet broad climate patterns like El Ni~no, tracked months before they alter hydro-climatic conditions, are still not incorporated as an early-warning component in food-security responses. We address this gap by introducing sensitivity regimes, a stratification of regions by the direction and strength of their vegetation response to the El Ni~no Southern Oscillation, and using them to estimate how food price spikes affect acute food insecurity across sub-Saharan Africa. Integrating remote sensing, socioeconomic data, and causal machine learning, we find that in regions where ENSO systematically suppresses vegetation, a price spike raises the share of the population at acute risk by 5.4 percentage points in the following month. In regions where vegetation is unaffected by or positively linked to ENSO, the estimated effect is smaller (around 2 percentage points) and statistically insignificant. These results demonstrate that climate context is critical for understanding food security vulnerabilities. Sensitivity regimes can be combined with operational price-spike triggers to stage anticipatory action: the ENSO state flags vulnerable regions months ahead, and a pre-positioned response in those regions to a price spike would avert the largest jump in acute food insecurity.