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Bayesian Networks for Health Economics and Public Policy Research

 

In this seminar, we illustrate how Bayesian networks can serve as a powerful modeling and reasoning framework for health economics research and public policy development.

For five different case studies, we present a complete analysis workflow using the BayesiaLab 8 software platform:

  • Diagnostic decision support: using a machine-learned Bayesian network for cost-effective evidence-seeking in diagnosing coronary heart disease. This example introduces information-theoretic measures, such as Entropy and Mutual Information.
  • Quantifying the value of information in field triage for optimizing trauma activation thresholds with regard to hospital resource utilization.
  • Developing universal health policies under extreme uncertainty, i.e., without any data: "test & treat" or presumptive malaria treatment in sub-Saharan Africa.
  • Childhood Literacy Campaign: Simpson's paradox rears its ugly head and leads to misguided policies.
  • Causal inference from observational healthcare data: using machine learning and the Disjunctive Cause Criterion to reduce—but not eliminate—the need for causal assumptions.

For each example, we present the motivation, proposed methodology, and practical implementation.

Seminar Materials

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