Video by Open Data Science and AI Conference via YouTube
Across nearly every country in the world, women live longer than men—but the size of this gap varies from about two years in some countries to more than twelve in others. What explains these differences, and how much of the gap can be closed?
In this talk, I present a practical approach to counterfactual analysis using Bayesian regression models. Using publicly available mortality data, we build a model that relates the life expectancy gap between men and women to differences in cause-specific death rates, including homicide, drug overdoses, traffic fatalities, smoking-related disease, and chronic illness.
The model generates posterior simulations that answer “what-if” questions. For example: How much smaller would the U.S. life expectancy gap be if homicide rates matched those in Western Europe?
The talk presents the workflow from assembling global datasets to fitting interpretable Bayesian models with PyMC and generating counterfactual simulations. Attendees will learn how Bayesian models can support explainable modeling and analysis under uncertainty.
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