Hierarchical Causal Graph-Based Methods for Imputing Food Insecurity

Food insecurity is typically modelled using inter-regional data comprising economical, geophysical and social variables. Such datasets are often of varying granularity, with each variable corresponding to a certain granularity level (e.g., GDP is a national variable, while disaster displacement can be local or regional). Additionally, each level shows a specific causal relation of its variables.  Since countries affected by food insecurity are usually underdeveloped, collecting such variables is a challenging task, leading to highly-incomplete datasets. To deal with the multi-level complexity and incomplete nature of the data, we propose to build a hierarchical causal graph (HCG) structure of the variables, that can then be injected in different imputation methods. Specifically, we propose to classify the variables at different granularity levels, and use causal graph discovery to learn a causal graph at each level. We test the proposed approach for imputing food insecurity using a dataset of 300+ economical, geophysical and social variables for more than 70 countries.