Summary
There is a growing interest in the intersection of causal inference and machine learning. On one hand, ML methods  e.g., prediction methods, unsupervised methods, representation learning  can be adapted to estimate causal relationships between variables. On the other hand, the language of causality could lead to new learning criteria that yield more robust and fair ML algorithms. In this course, we'll begin with an introduction to the theory behind causal inference. Next, we’ll cover work on causal estimation with neural networks, representation learning for causal inference, and flexible sensitivity analysis. We’ll conclude with work that draws upon causality to make machine learning methods fair or robust. This is an advanced course and students are expected to have a strong background in ML.
Covid19 Related Updates
 Classes to return to inperson mode starting Jan 31.

From UdeM: Classes to be fully online until Jan. 31.
 Classes to begin week of Jan 10.
Class Information

Start date: Jan. 11

When: Tuesdays, 12:30 to 2:30 PM and Fridays, 11:30 to 1:30 PM

Where:

Inperson: Auditorium 2, 2nd floor, Mila building, 6650 Rue SaintUrbain.

On Zoom: link on Studium.
 Attend in person if possible.

Office hours: Tuesdays, 2:30 to 3:30 PM, Room F.04 in the Mila building.
Assigned Readings (updated often)
* = under resources on Piazza

Jan 14: Chapters 18, 19 and 20, Advanced Data Analysis from an Elementary Point of View by Cosma Shalizi

Jan 18: Chapter 21 (on estimation), Advanced Data Analysis from an Elementary Point of View by Cosma Shalizi

Jan 21: Chapter 1 (on potential outcomes), Causal Inference for Statistics, Social, and Biomedical Sciences*, by Guido Imbens and Donald Rubin

Jan 25: Identification of Causal Effects Using Instrumental Variables*, by Joshua Angrist, Guido Imbens and Donald Rubin

Jan 28: Chapter 4 (on counterfactuals), Causal Inference in Statistics: A Primer,* by Judea Pearl, Madelyn Glymour and Nicholas Jewell
Topics covered
 Introduction to causality
 Causal graphical models
 Defining causal quantities: interventions and counterfactuals
 Identifying causal quantities: graphical criteria, and instrumental variables
 Estimating causal quantities
 ML helps causality
 Adapting neural networks for estimation
 Learning representations for causal inference
 Sensitivity analysis
 Causal discovery
 Causality helps ML
 Defining disentanglement
 Criteria for better outofdistribution generalization
 Criteria for fair prediction
Evaluation
 30%  Reader reports for assigned readings
 70%  Final project report
Prerequisites
I will assume programming experience and familiarity with topics taught in
Fundamentals of Machine learning (or equivalent). Background in probabilistic graphical models will be useful.
Resources