CSC2541 Topics in Machine Learning: Introduction to Causality

The Essentials

Instructor: Dr. Rahul Krishnan
Time: Mondays 3:00PM - 5:00PM.
Location: RW 140
Office Hours:
  • Rahul: Mondays, 11:00AM - 12:00PM, PT 286.
  • Vahid: Wednesdays, 1:30PM - 2:30PM, PT 286.
"Towards causal representation learning"., Schölkopf et al. Proceedings of the IEEE 2021


Course Description

There is an increasing interest in using machine learning to solve problems in causal inference and the use of causal inference to design new machine learning algorithms. In this course, we will discuss the difference between statistical and causal estimands and introduce assumptions and models that allow estimating causal queries. It will be a mixed lecture/assignment/seminar/project-based course. Students will learn the basic concepts, nomenclature, and results in causality, along with advanced material characterizing recent applications of causality in machine learning. The motivating examples for the course material will be the application of machine learning to problems in healthcare. Students must have a strong background in probability theory, statistics, Bayesian networks, and familiarity with linear algebra and latent variable modeling.

Teaching Staff

Rahul Krishnan
Rahul Krishnan
Teaching Assistants
Vahid Balazadeh
Vahid Balazadeh