Dr. Rina Dechter - University of California at Irvine ZOT!
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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

[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 Sign-Up Sheet

Week Topic Lectures
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,
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

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.