Dhanya Sridhar

Assistant Professor

Curriculum vitae

dhanya.sridhar <at> mila.quebec


University of Montreal, Mila

F.04, 6666 Rue St. Urbain

IFT 6168, Winter 2023

Causal Inference and ML


  • As indicated in the course information below, the first week of class (Jan 10 and Jan 14) will be cancelled. Instead, the course will run from Jan 17 until April 21.
  • We will use Piazza for all communication in this course. Enrolled students will receive a link to join the Piazza page via Studium by Jan 8, and auditing students will receive a link via email by then. 
  • Until all students have joined Piazza, I'll post all key announcements here so please check this webpage frequently.


Machine learning (ML), with its success in language understanding to biological settings, is a key ingredient to intelligent agents that help us with science and decision making. However, ML faces two major hurdles that limit its wider use. First, ML systems struggle to generalize out-of-distribution (OOD), to unseen tasks and domains. Second, ML systems learn correlations but science and decision making require causal inference – an inference about the effects of interventions. The field of causality, with its formalism of causal models, provides a theoretical framework to address the shortcomings of ML systems. Causality benefits from ML too: instead of carefully measuring variables of interest and defining causal models, with ML, we can learn infer quantities from rich sources of data.

In this course, we'll begin with an introduction to the theory of causal models. We'll build on this foundation and study the role causality plays in OOD generalization. Then, we'll study how techniques from ML such as prediction with NNs, representation learning, and gradient based optimization help us leverage large-scale, unstructured data to make causal inferences, from estimating effects to discovering causal models. We'll focus on the challenges and open research problems around learning causal variables and models from data using ML. This is an advanced course, taught seminar-style, and expects students to have a strong background in ML.

The course website from last year can provide a general idea of  how the course will be structured. However,  there will be different readings this year, and the grading scheme will change slightly.

Class Information

  • UdeM academic page
  • Start date: Jan. 17, 2023
  • End Date:  April 21, 2023
  • When: Tuesdays, 12:30 to 2:30 PM and Fridays, 11:30 to 1:30 PM
  • Where:  Auditorium 2, 2nd floor, Mila building, 6650 Rue Saint-Urbain
  • Office Hours: TBD
  • Class size: ~35
  • Auditing policy: Auditors will be allowed on a first-come, first-serve basis depending on the enrollment. However, auditing students must complete all course work including the course project.


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.

Assigned reading


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