80-617 Formal Methods: Causation

6 units, Carnegie Mellon University

Why do we care about causality? How can we learn causality from observational data? What role does causality play in machine learning under data heterogeneity? Is causality an essential component in general-purpose artificial intelligence? This mini is concerned with understanding, learning, and using causality, and focused on using probabilistic graphical models to represent causal relations and infer causal effects, causal discovery from observational data, and how to discover and exploit causal models and other related representations to achieve artificial intelligence.

Prerequisites: none listed

Offered: Fall 2025

Courseweave needs JavaScript to show sections, workloads and planning tools.