Dr. Rina Dechter - University of California at Irvine ZOT!
home | publications | book | courses | research Revised on Feb. 23, 2024



CS 295 - Winter 2022-23, Causal Inference
| main |
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:
(You will need to be logged into Google with you UCI account to access.)
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.