Dr. Rina Dechter @ UCI

Dr. Rina Dechter - University of California at Irvine
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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 second-year 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 Back-Door 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:

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Project Information
Project Sign-Up Sheet

Syllabus:

Week	Topic	Lectures	Slides	Homework	Reading	Date

Week 1	Introduction: Pearl's Causal Hierarchy Simpson Paradox, Causal Hierarchy, Defining Structural Causal Models	Lec 1 Lec 2 Lec 3	Slides 1		[W] Ch. 1 [P] Ch. 1 [PCH] Sec 1.1	M 03/29 W 03/31 F 04/02

Week 2	Structural Causal Models, d-Separation Identification of Causal Effects	Lec 4 Lec 5 Lec 6	Slides 2 Slides 3	HW 1	[P] Ch. 2 [PCH] Sec 1.2 [Darwiche]: Ch. 4	M 04/05 W 04/07 F 04/09

Week 3	Identification of Causal Effects The Back-Door Criterion	Lec 7 Lec 8			[P] Ch. 3 [C] Sec 1.3, 3.1-3.3	M 04/12 W 04/14 F 04/16

Week 4	The Front-Door Criterion The DO Calculus	Lec 9 Lec 10 Lec 11	Slides 4	HW 2	[C] Ch. 3.4-3.5 [P] Ch. 3 Biometrika 1995	M 04/19 W 04/21 F 04/23

Week 5	Linear Structural Causal Models	Lec 12 Lec 13	Slides 5 Slides 5b		[P] Ch. 3.8 [C] Ch. 5	M 04/26 W 04/28 F 04/30

Week 6	Counterfactuals	Lec P1 Lec 14 Lec 15	Slides P1 Slides 6b		[P] Ch. 4 [C] Ch. 7	M 05/03 W 05/05 F 05/07

Week 7	Causal Structure Discovery Bucket Elimination Fri (Normal Class Time): MIT Thesis Defense (ML and Causal Inference)	Lec P2 Lec 16 Thesis Def. Zoom	Slides P2 Slides 7	HW 3	Theory of Inferred Causation Causation, Prediction, and Search (Ch. 5)	M 05/10 W 05/12 F 05/14

Week 8	Detecting Latent Heterogeneity Algorithmic Approach to Identification	Lec P3 Lec 17	Slides P3 Slides 8		Detecting Latent Heterogeneity	M 05/17 W 05/19 F 05/21

Week 9	Causality for Machine Learning The Causal Foundation of Applied Probability and Statistics Causal Models for Dynamical Systems	Lec P4 Lec P5 Lec P6	Slides P4 Slides P5 Slides P6		Causality for Machine Learning - Paper Causality for Machine Learning - Chapter The causal foundations of applied probability and statistics Causal Models for Dynamical Systems	M 05/24 W 05/26 F 05/28

Week 10	Recovering Probabilistic and Causal Queries from Missing Data Independent Choices and Deterministic Systems	Lec P7 Lec P8	Slides P7		Recovering Probabilistic and Causal Queries from Missing Data Independent Choices and Deterministic Systems	M 05/31 [holiday] W 06/02 F 06/04

Finals Week	TBD					W 06/09

Background Reading

[DF] Elias Bareinboim and Judea Pearl,
     Causal inference and the data-fusion 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 semi-Markovian 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.