Bayesian Regression: Theory & Practice

Causal inference (w/ Bayesian regression)

This unit introduces the basics of causal inference, following the approach of Judea Pearl. The material presented here draws heavily on this excellent primer. What we add to this is the Bayesian-regression perspective. We show how to quantify uncertainty of estimates of causal effects, using Bayesian regression modeling.

There is a short exercise script on stochastic dependence and causal relation, and a tutorial script on causal inference.