Instructor: Prof. Elena Zheleva

Contact:

Time: TTh 3:30-4:45pm

Location: TBH 180D

Office hours: Tue 1:30-3pm in SEO 1140

Two roads diverged in a yellow wood,
And sorry I could not travel both [...]
I took the one less traveled by,
And that has made all the difference.
--Robert Frost

"The dramatic success in machine learning has led to an explosion of AI applications and increasing expectations for autonomous systems that exhibit human-level intelligence. These expectations, however, have met with fundamental obstacles that cut across many application areas. [... One of the obstacles] concerns the understanding of cause-effect connections. This hallmark of human cognition is [...] a necessary (though not sufficient) ingredient for achieving human-level intelligence. This ingredient should allow computer systems to choreograph a parsimonious and modular representation of their environment, interrogate that representation, distort it by acts of imagination and finally answer "What if?" kind of questions. Examples are interventional questions: ΄What if I make it happen?‘ and retrospective or explanatory questions: ΄What if I had acted differently?‘ or ΄what if my flight had not been late?‘ Such questions cannot be articulated, let alone answered by systems that operate in purely statistical mode, as do most learning machines today."
--From "The Seven Tools of Causal Inference with Reflections on Machine Learning" by Judea Pearl

Course Description

Causal reasoning is an integral part of data science and artificial intelligence. The goal of the course on Causal Inference and Learning is to introduce students to methodologies and algorithms for causal reasoning and connect various aspects of causal inference, including methods developed within computer science, statistics, and economics. The course will cover state-of-the-art research on causal reasoning and prepare students to conduct research in this area.

Course objectives

This is a seminar course. The goal of the course is to expose graduate students to state-of-the-art research on causal inference. The class project plays a central role in the course, and it should be taken as an opportunity to connect your research area of interest to the course topics.

Course format

Approximately one third of the course will be lecture-based using the following book by Judea Pearl, Madelyn Glymour and Nicholas Jewell: Causal Inference in Statistics: A Primer (Wiley Press 2016). The rest of the course will be student-led presentations of recent papers from the growing body of causal inference research.

The class will meet synchronously in person. We will be using Piazza for all course discussions and materials.

Student deliverables

Homework assignments
Written paper summaries
In-class participation
Research paper presentations
Course project (proposal, progress report, final presentation, final report)

Prerequisites

CS 412 Introduction to Machine Learning or consent of the instructor.

Textbooks

Required textbook:
[CISP] Judea Pearl, Madelyn Glymour, Nicholas Jewell (2016). Causal Inference in Statistics: A Primer. Wiley Press. Errata. (ebook from library). Solutions to selected problems. DAGitty solutions to selected problems.

Optional textbooks:
[WHY] Judea Pearl, Dana Mackenzie (2018): The Book of Why. Basic Books.
[CMRI] Judea Pearl (2009): Causality: Models, Reasoning, and Inference. Cambridge University Press. (ebook from library)
[ECI] Jonas Peters, Dominik Janzing, Bernhard Schölkopf (2017): Elements of Causal Inference: Foundations and Learning Algorithms. MIT Press.
[CPS] Spirtes, Glymour, Scheines (2000): Causation, prediction and search. MIT Press.
[CI] Hernan, Robins (2020). Causal inference. Chapman & Hall.
[CISSBS] Guido Imbens and Donald Rubin (2015): Causal Inference for Statistics, Social and Biomedical Sciences. Cambridge University Press.

Course schedule

Check Piazza for an up-to-date schedule.