Health Economics and Public Policy Research with Bayesian Networks
NYU Kimmel Center, 60 Washington Square South, Room, New York, NY 10012
Thursday, November 15, 2018, 1 p.m. – 4 p.m.
In this seminar, we will illustrate five prototypical examples of applied research with Bayesian networks and BayesiaLab related to health economics and public policy:
- Diagnostic decision support: using Bayesian networks for cost-effective evidence-seeking in diagnosing coronary heart disease.
- Quantifying the value of information in field triage for defining adaptive trauma activation thresholds for the optimal utilization of hospital resources.
- Developing universal policies under extreme uncertainty, e.g., in the absence of epidemiological data: "test & treat" vs. presumptive malaria treatment in sub-Saharan Africa.
- The Chicago Condom Campaign: Simpson's paradox rears its ugly head.
- Causal inference from observational healthcare data: using machine learning and the Disjunctive Cause Criterion to reduce—not eliminate—the need for causal assumptions from domain experts.
Each case study will include a theoretical presentation and a practical demonstration using the BayesiaLab software platform.
Preliminary Seminar Materials
- Example 1: Diagnostic Decision Support
- Example 2: Trauma Activation Policy
- Example 3: Optimizing Health Policies Under Uncertainty
- BayesiaLab Network File (XBL, 2 KB)
- Example 4: Chicago Condom Campaign
- Example 5: