The Optimal Policy Generator: A Causal Inference Protocol for Maximizing Median Health and Wealth Through Public Policy

Abstract: Centuries of public policy variation across thousands of jurisdictions (countries, states, cities) constitute a massive natural experiment. The data to identify which policies maximize welfare exists but has not been systematically harvested. The Optimal Policy Generator (OPG) applies causal inference methods (synthetic control, difference-in-differences, regression discontinuity) and Bradford Hill criteria to this cross-jurisdictional data, measuring policy impact on two welfare metrics: real after-tax median income growth and median healthy life years. For any jurisdiction, OPG produces four categories of public policy recommendations: ENACT (evidence-supported policies the jurisdiction lacks), REPLACE (policies set at suboptimal levels), REPEAL (policies with net welfare harm), and MAINTAIN (policies aligned with evidence). Each recommendation includes expected effects on both metrics, confidence grades, and blocking factors including freedom and autonomy constraints. The framework is agnostic to which party enacted each policy, evaluating only whether it improved outcomes. Projected welfare gains under framework assumptions: 5-15% of GDP for typical US states (90% CI: 2-25%), pending retrospective validation. Summary: The Optimal Policy Generator (OPG) produces systematic public policy recommendations for jurisdictions at any level (country, state, city), generating prioritized enact/replace/repeal/maintain recommendations to maximize real after-tax median income growth and median healthy life years, based on quasi-experimental evidence from centuries of policy variation data.