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CS 295  Spring 2021, Causal Reasoning  
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Instructor: Rina Dechter Days,Time: M/W/F, 9:00 am  9:50 am (PT) Classoom: https://uci.zoom.us/j/99900117495 Office hours: Upon Request
Course Description
This course will cover topics in Causal Inference. The course will run as a seminar
where I will give lectures in the first half of the course and then students
will be required to read and present papers based on chapters in books to the class
for the second half.
There will be a course project which can be based on these selected papers and also
several homework assignments. This course is intended for PhD students
in the area of AI and Machine Learning, with 271 and 273 as prerequisite courses.
Additionally, students who took CS 276 will have particularly good preparation for
this course. If you are a secondyear master student that already took 271 and 273,
please talk to me to obtain approval.
Course Topics
• Introduction: Causal Hierarchy • The Simpson Paradox • Structural Causal Models • Identification of Causal Effects • The Problem of Confounding and the BackDoor Criterion • Causal Calculus • Linear Structural Causal Models • Counterfactuals • Structural Learning
Textbooks
[P] Judea Pearl, Madelyn Glymour, Nicholas P. Jewell,Causal Inference in Statistics: A Primer, Cambridge Pess, 2016. [C] Judea Pearl, Causality: Models, Reasoning, and Inference, Cambridge Press, 2009. [W] Judea Pearl, Dana Mackenzie, The Book of Why, Basic books, 2018. [PCH] E. Bareinboim, J. Correa, D. Ibeling, T. Icard, On Pearl's Hierarchy and the Foundations of Causal Inference, Columbia University, 2020. (To appear in: Probabilistic and Causal Inference: The Works of Judea Pearl, ACM Turing Series). [Darwiche] Adnan Darwiche, Modeling and Reasoning with Bayesian Networks, Cambridge Press, 2009.
Course Project:
(You will need to be logged into Google with you UCI account to access.)
Project Information Project SignUp Sheet
Syllabus:
Background Reading
[DF] Elias Bareinboim and Judea Pearl,Causal inference and the datafusion problem, PNAS, 2016. Jin Tian, Studies in Causal Reasoning and learning, University of California, Los Angeles, 2002. Jin Tian and Judea Pearl, A General Identification Condition for Causal Effects, AAAI, 2002. [PCH] E. Bareinboim, J. Correa, D. Ibeling, T. Icard, On the completeness of an identifiability algorithm for semiMarkovian models, Annals of Mathematics and Artificial Intelligence, 2008. Karthika Mohan and Judea Pearl, Graphical Models for Processing Missing Data, Journal of the American Statistical Association, 2021. [BMK] Judea Pearl, Causal diagrams for empirical research, Biometrika, 1995. Daniel Kumor, Carlos Cinelli, Elias Bareinboim, Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets, NeurIPS, 2019. Judea Pearl, Linear Models: A Useful “Microscope” for Causal Analysis, Journal of Causal Inference, 2013. 