CAUSE-Twin bridges double machine learning, causal DAG identification, and multi-country panel data to allow international development agencies to model, test, and optimize health policy interventions before deploying real-world capital.
Every year, millions of preventable child deaths occur in developing nations due to compounding systemic factors: malnutrition, lack of sanitation, low maternal literacy, and economic instability.
Standard predictive AI models frequently fail policymakers because they identify **correlations** rather than **true causal levers**. Investing capital based purely on predictive correlation risks wasting scarce development funding without saving lives.
"CAUSE-Twin solves this by decoupling confounding baseline trends from true causal treatment effects using Double Machine Learning (DML) and DoWhy backdoor adjustments."
Uses authentic World Bank Crude Birth Rates (CBR) and UN IGME child mortality statistics.
Estimates Conditional Average Treatment Effects (CATE) tailored to each nation's context.
Tests multi-variable policy shifts (Nutrition, Sanitation, Literacy, GDP) in real time.
Exposes estimation confidence intervals, zero-crossings, and extrapolation bounds.