Time: TTh 1112:15pm
Location: Taft Hall 216
Instructor: Prof. Elena Zheleva
Office hours: TBD, SEO 1140
Contact:
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 humanlevel intelligence. These expectations, however,
have met with fundamental obstacles that cut across many application areas. [... One of the obstacles] concerns the understanding of causeeffect connections. This hallmark of human cognition is [...] a necessary (though not sufficient) ingredient for achieving humanlevel 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
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 stateoftheart research on causal reasoning and prepare students to conduct research in this area.
This is a seminar course. The goal of the course is to expose graduate students to stateoftheart 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.
Approximately one third of the course will be lecturebased 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.
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.
Date  Topic  Assigned Reading  Presenter  Announcements 
8/27  Introduction 
Syllabus Philosophy of causality 
Professor  
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. 
Professor  
9/3  Hypothesis testing and randomized controlled trials 
CIT Ch. 11 (book list at the end of table) PTDS Ch. 18 
Professor  
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, backdoor criterion  CISP Ch. 3.13.3  Professor  
9/19  Frontdoor criterion, covariatespecific effects, inverse probability weighing  CISP Ch. 3.43.6  Professor  HW 1 due 11:59pm 
9/24  Mediation and causal inference in linear systems  CISP Ch. 3.73.8  Professor  
9/26  Defining and computing counterfactuals  CISP Ch. 4.12.1  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. 4.44.5  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. Multilevel causeeffect 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 multicause causal inference with unobserved confounding: Counterexamples, impossibility, and alternatives. AISTATS 2019. Wang, Blei. The blessings of multiple causes. Arxiv 2018. Code. 
Professor  
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. AAAIWHY 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, JeanPhilippe  
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 metatransfer 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 