Causal evaluation of humanitarian aid on food security

In a world where climate change is rapidly accelerating, droughts are becoming more frequent and severe, posing a serious challenge to food security in the most vulnerable regions. The Horn of Africa has witnessed a rise in acute malnutrition, affecting 6.5 million people in 2022 [1]. Prolonged dry spells significantly contribute to this crisis [2], yet it is crucial to recognize that droughts are not the sole driver. Various factors, including hydrological conditions, food production capabilities, market access, insufficient humanitarian aid, conflicts, and displacement, play a significant role [3,4]. Understanding the underlying causes of food insecurity is pivotal for improving the effectiveness of humanitarian actions, yet in this context, the study proves to be complex, involving multiple variables, scales, and non-linear relationships. Predictive Machine Learning (ML) techniques are not suited to understanding the causes and estimating the causal effect by default [5,6], instead, this study focuses on causal inference to quantify the impacts of climate and socioeconomic factors on food insecurity. Our key contributions involve discerning causal relationships within the intricate food security system, integrating a comprehensive database including socio-economic, weather and remote sensing data, and estimating the causal effect of humanitarian interventions on the food security index, the outcome of interest. The causal discovery task is performed via time series methods accounting for nonlinear and nonstationary relations, like the PCMCI algorithm and nonlinear Granger causality [7,8], identifying the drivers in the data that are causally linked to the outcome. Besides, the causal effect estimation task is performed via a Conditional Average Treatment Effect (CATE), gaining insights into the spatiotemporal heterogeneity of the impact of humanitarian interventions on the outcome [9]. Such endeavors are crucial for facilitating more efficient future interventions and policies, thereby improving transparency and accountability in humanitarian aid.References[1] WFP, “Impacts of the Cost of Inaction on WFP Food Assistance in Eastern Africa (2021 & 2022),” docs.wfp.org, 2023.[2] Coughlan de Perez E., et al, “From rain to famine: assessing the utility of rainfall observations and seasonal forecasts to anticipate food insecurity in East Africa,” Food Secur., vol. 11, no. 1, pp. 57–68, 2019.[3] Maxwell D. et al, “Viewpoint: Determining famine: Multi-dimensional analysis for the twenty-first century,” Food Policy, vol. 92, 2020.[4] Guy A. J. et al, “Climate, conflict and forced migration” Global Environmental Change, vol. 54, no. 4, 2019.[5] Pearl J., “Causality: Models, reasoning, and inference,” Cambridge University Press, vol. 19, 2000.[6] Peters J., Janzing D., and Schlkopf B., Elements of Causal Inference: Foundations and Learning Algorithms, The MIT Press, 2017.[7] Runge, J.. "Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets." Conference on Uncertainty in Artificial Intelligence. PMLR, 2020.[8] Camps-Valls, G. et al, “Discovering causal relations and equations from data”, Physics Reports 1044 :1--68, 2023[9] Giannarakis, G. et al, (2022). Personalizing sustainable agriculture with causal machine learning. arXiv preprint arXiv:2211.03179.