Assessing the Causal Impact of Humanitarian Aid on Food Security
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| author | Cerdà-Bautista, Jordi Tárraga, José María Sitokonstantinou, Vasileios Camps-Valls, Gustau |
| author_facet | Cerdà-Bautista, Jordi Tárraga, José María Sitokonstantinou, Vasileios Camps-Valls, Gustau |
| contents | In the face of climate change-induced droughts, vulnerable regions encounter severe threats to food security, demanding urgent humanitarian assistance. This paper introduces a causal inference framework for the Horn of Africa, aiming to assess the impact of cash-based interventions on food crises. Our contributions include identifying causal relationships within the food security system, harmonizing a comprehensive database including socio-economic, weather and remote sensing data, and estimating the causal effect of humanitarian interventions on malnutrition. On a country level, our results revealed no significant effects, likely due to limited sample size, suboptimal data quality, and an imperfect causal graph resulting from our limited understanding of multidisciplinary systems like food security. Instead, on a district level, results revealed significant effects, further implying the context-specific nature of the system. This underscores the need to enhance data collection and refine causal models with domain experts for more effective future interventions and policies, improving transparency and accountability in humanitarian aid. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_11287 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Assessing the Causal Impact of Humanitarian Aid on Food Security Cerdà-Bautista, Jordi Tárraga, José María Sitokonstantinou, Vasileios Camps-Valls, Gustau Machine Learning In the face of climate change-induced droughts, vulnerable regions encounter severe threats to food security, demanding urgent humanitarian assistance. This paper introduces a causal inference framework for the Horn of Africa, aiming to assess the impact of cash-based interventions on food crises. Our contributions include identifying causal relationships within the food security system, harmonizing a comprehensive database including socio-economic, weather and remote sensing data, and estimating the causal effect of humanitarian interventions on malnutrition. On a country level, our results revealed no significant effects, likely due to limited sample size, suboptimal data quality, and an imperfect causal graph resulting from our limited understanding of multidisciplinary systems like food security. Instead, on a district level, results revealed significant effects, further implying the context-specific nature of the system. This underscores the need to enhance data collection and refine causal models with domain experts for more effective future interventions and policies, improving transparency and accountability in humanitarian aid. |
| title | Assessing the Causal Impact of Humanitarian Aid on Food Security |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2310.11287 |