Course syllabus for Causality and machine learning

Course syllabus adopted 2024-02-01 by Head of Programme (or corresponding).

Overview

  • Swedish nameKausalitet och maskininlärning
  • CodeDAT465
  • Credits7.5 Credits
  • OwnerMPDSC
  • Education cycleSecond-cycle
  • Main field of studyComputer Science and Engineering, Software Engineering, Mathematics
  • DepartmentCOMPUTER SCIENCE AND ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

The course round is cancelled. For further questions, please contact the director of studies
  • Teaching language English
  • Application code 87112
  • Open for exchange studentsYes

Credit distribution

0121 Written and oral assignments 7.5 c
Grading: TH		7.5 c0 c0 c0 c0 c0 c

In programmes

Examiner

Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

A course in programming in a general-purpose language (e.g. C/C++/Java/Python). One course each in calculus, linear algebra and mathematical statistics. We strongly recommend that the student has taken a course in Machine learning, for example DAT340, TDA233 or similar, or that such a course is taken in parallel alongside this course. If you have completed the prerequisites but feel that you may need a refresher, make sure to be comfortable with basic definitions and properties of random variables, probability distributions and densities, expectations and basic linear algebra such as matrix and vector products and norms.

Aim

The aim for this course is to give knowledge and understanding of causality and causal inference from a mathematical, statistical and computational perspective. It should also provide the tools necessary for solving causal inference problems in practical applications. 

Learning outcomes (after completion of the course the student should be able to)

On successful completion of the course the student will be able to:

Knowledge and understanding
  • Provide an overview over different frameworks for causality and causal inference
  • Describe important causal problems and give examples of their applications
  • Explain how problems of causal inference differs from other types of (statistical) inference problems
  • Account for common approaches to causal inference and the conditions under which they are accurate

Skills and abilities
  • Identify different types of causal inference problems in real-world applications
  • Analyze causal inference problems and estimate the plausibility of solving them under certain conditions
  • Implement and apply methods for causal inference appropriate for specific problems

Judgement and approach
  • Discuss pros and cons of different frameworks for causality and causal inference
  • Reflect over the fundamental limitations to the possibility of causal identification
  • Critically analyse and discuss research and applications of causal inference, in particular with respect to adjustment for confounding factors

Content

The content is structured into 8 modules, one for each week of the course. We begin by briefly covering necessary prerequisites, such as probabilistic graphical models, and by introducing structural definitions of causality. This will enable us to study sufficient conditions for inferring causal relationships between random variables. In the second half of the course, we study estimation of causal effects and policy evaluation. These are important topics in, e.g., epidemiology. At the end of the course, each student will present a research paper on a topic of their choice.
  • Probabilistic graphical models
  • Causality
  • Structural causal models (SCM)
  • Causal structure learning
  • Neyman-Rubin causal model (potential outcomes)
  • Observational studies
  • Policy evaluation
  • Mediation analysis

Organisation

Modules composed of lectures, follow-up sessions and hand-in assignments. Follow-up sessions are interactive meetings focused on problem solving and advanced aspects of the module topic. The course concludes with research paper presentations by students.

Literature

The course will be based on public material such as research papers and ebooks available online. There will be weekly in-class or video lectures given by an instructor. If you would like to read a textbook on the subject, consider one of the following:

- Pearl, Judea. Causality. Cambridge university press, 2009.
- Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/
- Morgan, Stephen L., and Christopher Winship. Counterfactuals and causal inference. Cambridge University Press, 2015. 2nd Edition.

Examination including compulsory elements

Assignments and paper presentations. 

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.