Collaborative Causal and Bayesian Modeling
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This is a collection of some applied work with particularly interesting Bayesian and/or causal modeling. For instance, Hubbard et al. use a Bayesian mixture model to infer patients' unknown diabetes status in noisy EHR data. Harrison et al. use a Bayesian hierarchical zero-inflated model to assess difference in costs associated with a cost-lowering healthcare intervention. Takvorian et al. use a difference-in-differences strategy to assess the impact of medicare expansion under ACA on cancer treatment delivery.