Assessing the Causal Impact of Humanitarian Aid on Food Security
Jordi Cerdà-Bautista, José María Tárraga, Vasileios Sitokonstantinou, Gustau Camps-Valls
TL;DR
This paper develops a DAG-based causal-inference framework to assess cash-based interventions on malnutrition amid Horn of Africa droughts, combining harmonized climate, socio-economic, and conflict data. Using do-notation and back-door adjustment, it estimates the Average Treatment Effect across country- and district-level settings, with DoWhy and CausalML methods and refutation tests. While country-level results are not statistically significant, district-level analyses show context-specific effects, highlighting data quality and localization as key determinants of causal detectability. The work advances humanitarian analytics by proposing data-driven causal discovery via the CauseMe platform and advocating for better data collection, domain-expert-guided causal graphs, and localized intervention strategies to improve transparency and effectiveness.
Abstract
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.