CS 594 Causal Inference and Learning
University of Illinois at Chicago, Fall 2019

Time: TTh 11-12:15pm

Location: Taft Hall 216

Instructor: Prof. Elena Zheleva

Office hours: TBD, 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 cover recent papers from the growing body of research on causal inference.

Student deliverables

Homework - 15%
Paper summaries and discussion - 15%
Presentations - 20%
Course project - 50% (proposal, progress report, final presentation, final report)


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

We will be using Piazza for all course discussions and materials. Students registered for the course will be sent an enrollment email before the first day of class.

Course schedule (check Piazza for most up-to-date)

Date Topic Assigned Reading Presenter Announcements
8/27 Introduction
Philosophy of causality
8/29 Why causality?
Judea Pearl. The Seven Pillars of Causal Reasoning with Reflections on Machine Learning. Communications of the ACM. 2019.
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, Samuel J. Gershman. Building Machines That Learn and Think Like People. 2016.
9/3 Hypothesis testing and randomized controlled trials CIT Ch. 11 (book list at the end of table)
PTDS Ch. 18
9/5 Statistical models: review CISP Ch. 1 Professor
9/10 Structural causal models CISP Ch. 1 Professor
9/12 Graphical models CISP Ch. 2 Professor HW 1 out
9/17 Interventions, adjustment formula, back-door criterion CISP Ch. 3.1-3.3 Professor
9/19 Front-door criterion, covariate-specific effects, inverse probability weighing CISP Ch. 3.4-3.6 Professor HW 1 due 11:59pm
9/24 Mediation and causal inference in linear systems CISP Ch. 3.7-3.8 Professor
9/26 Defining and computing counterfactuals CISP Ch. 4.1-2.1 Professor Project proposal due
10/1 Counterfactual probabilities, counterfactuals in linear systems CISP Ch. 4.3 Professor
10/3 Counterfactuals: attribution, mediation, practical uses CISP Ch. 4.4-4.5 Professor HW 2 out
10/8 Causal entropy
Ziebart, Bignell, Dey. The principle of maximum causal entropy for estimating interacting processes. IEEE Transactions on Information Theory 2013.
Guest lecture:
Prof. Brian Ziebart
10/10 Selection and transportability bias
Bareinboim, Pearl. Causal inference and the data fusion problem. PNAS 2016.
Bareinboim, Tian, Pearl. Recovering from selection bias in causal and statistical inference. AAAI 2014. (best paper award)
Guest lecture:
Prof. Ali Tafti
10/15 Causal discovery I
CPS Ch. 5
Sridhar, Pujara, Getoor. "Scalable probabilistic causal structure discovery." IJCAI 2018. Code.
Shishir, Moh
10/17 Causal discovery II
Chalupka, Perona, Eberhardt. Multi-level cause-effect systems. AISTATS 2016. Code.
Beckers, Eberhardt, Halpern. Approximate causal abstraction. UAI 2019.
Aditi, Ragib HW 2 due 11:59pm
10/22 Confounding bias
D'Amour. On multi-cause causal inference with unobserved confounding: Counterexamples, impossibility, and alternatives. AISTATS 2019.
Wang, Blei. The blessings of multiple causes. Arxiv 2018. Code.
10/24 Interference bias
Ogburn, VanderWeele. Causal diagrams for interference. Statistical science. 2014.
Rushit, Harikrishna
10/29 Causal inference in networks I
Bhattacharya, Malinsky, Shpitser. Causal inference under interference and network uncertainty. UAI 2019.
Arbour, Garant, Jensen. Inferring network effects in observational data. KDD 2016.
Ben, Chris
10/31 Causal inference in networks II
Shalizi, Thomas. Homophily and contagion are generically confounded in observational social network studies. Sociological Methods and Research, 40, 2011.
Zahra, Zohreh
11/5 Causal inference in social sciences
Varian. Causal inference in economics and marketing, PNAS 2016.
Shmueli. To explain or to predict?, Stat. Science 2010.
Athey. Impact of machine learning on economics. Chapter in The Economics of AI: An Agenda. 2018.
Professor Progress report due 11:59pm
11/7 Causal inference in networks III
Toulis, Kao. Estimation of causal peer influence effects. ICML 2013.
Eckles, Kizilcec, Bakshy. Estimating peer effects in networks with peer encouragement designs. PNAS 2016.
Matteo, Niccolo
11/12 Matching and propensity modeling
Shahid, Zheleva. Counterfactual learning in networks: An empirical study of model dependence. AAAI-WHY 2019.
Stuart. Matching methods for causal inference: a review and look forward. Stat. Science 2010.
Badhru, Kathy
11/14 Causal inference and NLP
Wang, Culotta. When do words matter? Understanding the impact of lexical choice on audience perception using individual treatment effect estimation. AAAI 2019.
Landeiro, Tran, Culotta. Discovering and Controlling for Latent Confounds in Text Classification Using Adversarial Domain Adaptation. SDM 2019.
Guest lecture:
Prof. Aron Culotta
11/19 Heterogeneous treatment effects
Künzel, Sekhon, Bickel, Yu. Metalearners for estimating heterogeneous treatment effects using machine learning. PNAS 2019.
Tran, Zheleva. Learning triggers for heterogeneous treatment effects. AAAI 2019.
Elisa, Jean-Philippe
11/21 Causal inference and machine learning
Scholkopf, Janzing, Peters, Sgouritsa, Zhang, Mooij. On causal and anticausal inference. ICML 2012.
Peters, Janzing, Scholkopf. Identifying cause and effect on discrete data using Additive Noise Models. AISTAT 2010.
Rishabh, Jishnu
11/26 Causal inference, reinforcement and transfer learning
Lee, Bareinboim. Structural causal bandits: where to intervene. NIPS 2018.
Bengio, Deleu, Rahaman, Rosemary, Lachapelle, Bilaniuk, Goyal, Pal. A meta-transfer objective for learning to disentangle causal mechanisms. Arxiv 2019. Code.
Kathy, Ishan
11/28 No class (Thanksgiving)
12/3 Presentations
12/5 Presentations
12/9 Finals week Final project due 11:59pm

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.
[PTDS] Lau, Gonzalez, Nolan: Principles and techniques of data science.
[CIT] Adhikari, DeNero: Computational and Inferential thinking.