Dr. Rina Dechter - University of California at Irvine ZOT!
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CS 276 - Causal and Probabilistic Reasoning with Graphical Models
2024, Fall (Q1)
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Course Reference

Course Description

One of the main challenges in building intelligent systems is the ability to perform causal inference under uncertainty. Graphical models, which include Bayesian networks and their extension into structural causal models offer a powerful and successful methodology of operationalizing causal inference in a wide spectrum of applications. Intelligent systems based on Bayesian networks are being used in a variety of real-world applications including diagnosis, sensor fusion, on-line help systems, credit assessment, bioinformatics and data mining. Causal reasoning is at the core of human reasoning and thus should play a central part in artificial intelligence and in machine learning.

The objective of this course is to provide an in-depth exposition of causal reasoning under uncertainty using structural causal models and Bayesian networks. Both theoretical underpinnings and practical considerations will be covered, with a special emphasis on exploring reasoning within the ladder of causation that include 1. Association, 2. Intervention, 3. Counterfactual.

Prerequisites

  • Familiarity with basic concepts of probability theory.
  • Knowledge of basic computer science, algorithms and programming principles.
  • Previous exposure to AI is desirable but not required.

Target Students:

This course is intended for PhD students in the area of AI, Machine Learning, and Statistics, and also appropriate for students from other disciplines with interest in causality and having relevant AI/ML/Statistics background.

Course Topics

  1. Probabilistic Graphical Models, Structural causal models,The Causal Hierarchy.
  2. Bayesian and Markov Networks: Representing independencies by graphs. d-seperation.
  3. Algorithms for probabilistic reasoning (Bucket-elimination for summation and optimization, Join-trees, The induced-width.).
  4. Sampling schemes for graphical models (MCMC and Importance Sampling).
  5. Structural Causal Models; Identification of Causal Effect; The problem of confounding.
  6. The Back-Door and Front-Door Criteria and the Do-Calculus.
  7. Linear Causal Models.
  8. Counterfactuals.
  9. Algorithms for identification using c-components. The ID algorithm.
  10. Learning Bayesian networks and Causal graphs (causal discovery).

Homework Assignments:

There will be four homework, each given roughly a week to complete.

Course Project:

Each student will also be engaged in a project based on papers from recent literature. The project will involve learning about and preseting an assigned paper/literature in class and writing a project report.

Project Information
Project Signup Page

Grading Policy:

Homework (70%), Course Project (15% presentation + 15% report = 30% total)


Syllabus

s
Week Topic Lectures
 Slides
 Homework
 Reading
 Date  
Week 0

  • Introduction

Lec 1
Slides 1
Th 09/26
via Zoom
Week 1

  • Probabilistic Graphical Models and Causal Bayesian Networks

  • Bayesian and Markov Networks: Representing independencies by graphs

Lec 2


Lec 3
Slides 2
HW 1
[Darwiche] Ch. 1, 3, 4


[Russell-Norvig] Ch. 13
Tues 10/01
via Zoom
Thurs 10/03
via Zoom
Week 2

  • Bayesian and Markov Networks: Representing independencies by graphs

  • The notion of d-separation

Lec 4


Lec 5
Slides 3


Slides 4




[Darwiche] Ch. 4


[Darwiche] Ch. 5
Tues 10/08


Thurs 10/10
Week 3

  • Variational algorithms for probabilistic graphical models (Bucket-elimination for summation and optimization)

Lec 6

Lec 7
Slides 5
HW 2
[Dechter] Ch. 4, 5.1-5.2

[Darwiche] Ch. 6
Tues 10/15

Thurs 10/17
Week 4

  • Tree decompositions and the cluster-tree elimination scheme
  • AND/OR Search

Lec 8
Slides 6


Slides 7

[Dechter] ch. 3.4

[Primer] ch. 1, ch 2
Tues 10/22

Thurs 10/24

Week 5

  • Structural Causal Models; Identification of Causal Effect; The problem of confounding
  • The Back-Door Criterion

Lec 10


Lec 11
Slides 8


Slides 9


HW 3
[Primer] Ch. 3

[causality] 1.3, 3.1-3,3
Tues 10/29

Thurs 10/31

Week 6

  • Front-Door
  • Do-Calculous

Lec 12

Lec 13
Slides 10

[Primer] ch. 3.4-3.6

[Causality] 3.4

Tues 11/05

Thurs 11/07

Week 7

  • Algorithms for identification using c-components

Lec 14

Lec 15
Slides 11

Slides 12




HW 4
[Primer] Ch. 4

[Causality] Ch. 7

Tues 11/12

Thurs 11/14

Week 8

  • Linear Structural Causal Models

  • Counterfacturals

Lec 16

Lec 17
Slides 13

_

Tues 11/19

Thurs 11/21

Week 9

  • Learning graphical models





_
Tues 11/26

No class 11/28

Week 10

  • Student Project Presentations





Tues 12/3

Thurs 12/5

Finals Week

  • Project Reports Due