A Causal Data Science Framework for Educational Displacement Under Extreme Resource Scarcity: Simulation-Based Evidence from Gaza (2023–2026)

This repository contains the manuscript and supplementary materials for the paper "A Causal Data Science Framework for Educational Displacement Under Extreme Resource Scarcity: Simulation-Based Evidence from Gaza (2023–2026)". The study introduces a causal data science framework combining causal inference with machine learning to estimate the effect of water and nutrition interventions on school attendance in conflict-affected settings. All parameters are calibrated from publicly available secondary data (UN, WHO, World Bank). The framework uses inverse probability weighting, double machine learning, and optimal policy trees to derive actionable intervention rules. The synthetic population and all analysis code are available in the associated GitHub repository (see "Related works" or code repository link). The manuscript is licensed under Creative Commons Attribution 4.0 International.