Evidence-based policy is only as effective as the evidence itself. By advancing rigorous causal inference, the Economic Science Society empowers lawmakers with unbiased insights.
Good governance requires separating true causal mechanisms from mere correlation. The ESS maintains that policies built on weak methodological foundations are vulnerable to unintended consequences. By deploying our diverse toolkit—from synthetic controls to causal machine learning—we ensure that policymakers have access to the highest standard of empirical evidence when evaluating past interventions and designing future programs.
Our members utilize rigorous causal analysis to evaluate the true economic impact of regulatory interventions on market competitiveness and consumer welfare. For example, causal estimators can precisely measure the downstream effects of preserving competitive consumer choice versus implementations of consolidated municipal service models.
As Artificial Intelligence reshapes the economy, the ESS leads in utilizing advanced data methodologies to guide its ethical regulation. By maintaining high standards of transparency and mitigating bias in complex datasets, our research informs the responsible integration of AI in both public infrastructure and private enterprise.
The global good requires swift, effective responses to crises. ESS scholars apply high-dimensional panel data and rigorous quasi-experimental designs to measure the exact causal effectiveness of localized health interventions and broad labor market policies.
The ESS is dedicated to making dense academic research accessible and actionable for decision-makers at the local, state, and federal levels.