Causal and Probabilistic Reasoning with Graphical Models

4 Units.

Provides in-depth exposition of causal reasoning and probabilistic graphical models theory and algorithms within Structural Causal Models and Bayesian Networks. Covers algorithms for Bayesian networks and central concepts (e.g., identifiability, the do-calculous, counterfactuals)