Dhanya Sridhar

Assistant Professor

Curriculum vitae

dhanya.sridhar <at> mila.quebec


University of Montreal, Mila

F.04, 6666 Rue St. Urbain

IFT 6168, Winter 2024

Causal Inference and ML


  • 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 7, 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.


This course combines lectures and seminar-style discussions to understand the fast-moving intersection of causality and machine learning (ML). The study of causality has long propelled fields like epidemiology, medicine, and economics. More recently, ML and causality are increasingly connected. On one hand, Parameterizing causal models with neural networks offers attractive out-of-distribution (OOD) guarantees as well as new ways of causally analyzing complex data. On the other hand, causal notions of intervening and inferring effects offer ways of analyzing mechanisms in large models. This course covers background on causal models, and recent results on core topics like causal representation learning, causal structure discovery, causal abstraction and uses of causality with large pre-trained models. The course will consist of readings (with associated write-ups) and a project.

Class information

  • Start date: January 8, 2024
  • End Date:  April 15, 2024
  • When: Mondays and Wednesdays, 12:30 to 2:30 PM
  • Where:  Auditorium 1, 2nd floor, 6650 Rue Saint-Urbain (Mila)
  • Auditing policy: Auditors will be allowed on a first-come, first-serve basis depending on the enrollment and their commitment to the course work. Auditing requests must be sent by email.
  • Mode: This course will be in-person only.


I will assume programming experience and familiarity with topics taught in Fundamentals of Machine learning (or equivalent).  Background on deep learning, unsupervised machine learning, and probabilistic graphical models will be very useful.

Assigned reading

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