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Listen "72 | Bayesian Methods Could Provide the Key to Answering Which Policies Work Best for Whom"
Episode Synopsis
On this episode of On the Evidence, Mathematica’s Mariel Finucane and John Deke join Tim Day of the Center for Medicare & Medicaid Innovation to discuss the application of evidence-informed Bayesian methods that not only confirm whether a policy or program works, but for whom.
Learn more about Mathematica's work using evidence-based Bayesian methods in applied policy research: https://mathematica.org/features/bayesian-methods
Read a brief about using a Bayesian framework for interpreting findings from impact evaluations prepared by Mariel Finucane and John Deke for the Office of Planning, Research and Evaluation at the Administration for Children and Families: mathematica.org/publications/moving-beyond-statistical-significance-the-basie-bayesian-interpretation-of-estimates-framework
Read a paper co-authored by Mariel Finucane that compares Bayesian methods with the traditional frequentist approach to estimate the effects of a Centers for Medicare & Medicaid Services demonstration on Medicare spending: mathematica.org/publications/revolutionizing-estimation-and-inference-for-program-evaluation-using-bayesian-methods
Read a paper co-authored by Tim Day describing an experiment to provide evidence that would be useful to policymakers and other decision makers through an interactive data visualization dashboard, presenting results from both frequentist and Bayesian analyses: https://www.researchgate.net/publication/335169870_Making_Evidence_Actionable_Interactive_Dashboards_Bayes_and_Health_Care_Innovation
Read Emily Oster’s newsletter article about why and how she applies Bayes’s Rule to interpret new evidence in the context of existing evidence, including a recent study (https://emilyoster.substack.com/p/does-pre-k-really-hurt-future-test) about the effects of a preschool program in Tennessee on future student test scores: https://emilyoster.substack.com/p/bayes-rule-is-my-faves-rule
Learn more about Mathematica's work using evidence-based Bayesian methods in applied policy research: https://mathematica.org/features/bayesian-methods
Read a brief about using a Bayesian framework for interpreting findings from impact evaluations prepared by Mariel Finucane and John Deke for the Office of Planning, Research and Evaluation at the Administration for Children and Families: mathematica.org/publications/moving-beyond-statistical-significance-the-basie-bayesian-interpretation-of-estimates-framework
Read a paper co-authored by Mariel Finucane that compares Bayesian methods with the traditional frequentist approach to estimate the effects of a Centers for Medicare & Medicaid Services demonstration on Medicare spending: mathematica.org/publications/revolutionizing-estimation-and-inference-for-program-evaluation-using-bayesian-methods
Read a paper co-authored by Tim Day describing an experiment to provide evidence that would be useful to policymakers and other decision makers through an interactive data visualization dashboard, presenting results from both frequentist and Bayesian analyses: https://www.researchgate.net/publication/335169870_Making_Evidence_Actionable_Interactive_Dashboards_Bayes_and_Health_Care_Innovation
Read Emily Oster’s newsletter article about why and how she applies Bayes’s Rule to interpret new evidence in the context of existing evidence, including a recent study (https://emilyoster.substack.com/p/does-pre-k-really-hurt-future-test) about the effects of a preschool program in Tennessee on future student test scores: https://emilyoster.substack.com/p/bayes-rule-is-my-faves-rule
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