Dr. Rina Dechter @ UCI

Dr. Rina Dechter - University of California at Irvine
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CS 295 - Winter 2022-23, Causal Inference

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Instructor: Rina Dechter
Days,Time: Tu/Th, 11:00 am - 12:20 pm (PT)
Classoom: MSTB 114
Zoom Link: https://uci.zoom.us/j/98005125009
Piazza Link: https://piazza.com/uci/winter2023/compsci295lecacau/home
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).
[D] 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 0	Introduction	Lec 0				Th 01/05

Week 1	Introduction: Pearl's Causal Hierarchy Simpson Paradox, Causal Hierarchy, Defining Structural Causal Models	Lec 1 Lec 2	Slides 1		[W] Ch. 1 [P] Ch. 1 [PCH] Sec 1.1	Tu 01/10 Virtual Class!!! Th 01/12 In-Person

Week 2	Structural Causal Models, d-Separation Identification of Causal Effects	Lec 3 Lec 4	Slides 2	HW 1 Due Sun 1/29	[P] Ch. 2 [PCH] Sec 1.2 [Darwiche]: Ch. 4	Tu 01/17 Th 01/19

Week 3	Identification of Causal Effects The Back-Door Criterion	Lec 5 Lec 6	Slides 3 Slides 4		[P] Ch. 3 [C] Sec 1.3, 3.1-3.3	Tu 01/24 Th 01/26

Week 4	The Front-Door Criterion The DO Calculus	Lec 7 Lec 8	Slides 5 Slides 6	HW 2 Due Sat 2/11	[C] Ch. 3.4-3.5 [P] Ch. 3 Biometrika 1995	Tu 01/31 Th 02/02

Week 5	Linear Structural Causal Models	Lec 9 Lec 10	Slides 5		[P] Ch. 3.8 [C] Ch. 5	Tu 02/07 Th 02/09

Week 6	Counterfactuals	Lec 12	Slides 6	HW 3 Due Tues 2/28	[P] Ch. 4 [C] Ch. 7	Tu 02/14 Th 02/16

Week 7	Algorithmic Approach for Identification	Lec 13 Lec 14	Slides 7 Project 1 Project 2		Theory of Inferred Causation Causation, Prediction, and Search(Ch. 5) External Validity- From Do-Calculus to Transportability Across Populations Ordinal Causal Discovery	Tu 02/21 Th 02/23

Week 8	Project Presentations	Lec 15 Lec 16	Project 3 Project 4 Project 5 Project 6 Project 7		Causality in Healthcare Estimating Identifiable Causal Effects through Double Machine Learning Instrumental Variables with Treatment-Induced Selection Direct and Indirect Effects Incorporating Causal Graphical Prior Knowledge into Predictive Modeling	Tu 02/28 Th 03/02

Week 9		Lec 17	Project 8 Project 9 Project 10 Project 11		Causal Discovery Causal Models for Dynamical Systems Recovering from Selection Bias in Causal Inference Detecting Latent Heterogenity	Tu 03/07 Th 03/09

Week 10						Tu 03/14 Th 03/16

Finals Week	TBD					Tu 03/21 10:30am-12:30pm

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.