Summary
Modern scientific, medical, and policy decisions increasingly rely on observational data to evaluate the effects of interventions such as medical treatments, public programs, and behavioral exposures. Many of these interventions are multi-level or continuous, such as medication dosage or program participation intensity, rather than simple binary choices. However, existing statistical methods, including widely used propensity score approaches, often become unstable or unreliable in these settings, particularly when applied to high-dimensional data with complex relationships. This project addres
