Abstract: Estimating the true causal effect of vaccination on clinical health outcomes using observational data is heavily confounded by individual risk perception and health behaviors. This paper outlines a structural quantitative framework using Two-Stage Least Squares (2SLS) regression to isolate the causal impact of COVID-19 vaccination (D) on severe clinical illness (Y) by leveraging an external policy shock—Vaccination-Differentiated Safe Management Measures (VDS)—as an instrumental variable (Z). This was the paper im referring to: https://pmc.ncbi.nlm.nih.gov/articles/PMC10162473/ Imagine you want to find out if the COVID vaccine physically causes a drop in severe illness and death. If you just compare vaccinated people to unvaccinated people, your data is completely poisoned by a hidden ghost variable: how naturally cautious a person is. The High-Risk Avoiders: People who are terrified of getting sick will aggressively line up to get vaccinated. But they also wear masks perfectly, wash their hands constantly, stay home, and avoid crowded areas. The Data Illusion: If you see low death rates among vaccinated people, you don't know how much of that is because of the medicine, and how much of that is just because they live a super cautious lifestyle. The data is a giant blur. To solve this without forcing people into a laboratory experiment, researchers look for an independent trigger that has absolutely nothing to do with personal health beliefs. This was the VDS (Vacci…

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