Simulation of synthetic health records for assessment of causal inference methods for vaccine efficacy
This paper demonstrates that low-fidelity synthetic datasets, generated from the EAVE-II platform using marginal structural models to simulate various confounding scenarios, can effectively validate causal inference methods for vaccine efficacy assessment, particularly showing that inverse probability of treatment weighting is necessary to recover true causal parameters under strong confounding.