Bayesian Nets

Macartan Humphreys (Columbia University and WZB)

July 3 - July 5

This course introduces Bayesian networks as a tool to map from formal theories to empirical structures and to locate strategies for inference from within-case data patterns to case level and population level causal claims. The sessions cover material on causal inference, on directed acyclic graphs, and Bayesian inference, and introduce R tools for design and inference such as daggity and stan.

The syllabus can be found here.