Course syllabus

Jump to today

Welcome to Causality and Causal Inference (DAT465/DIT654)!

Lp1 HT23 (7.5 hp)

Examiner & lecturer:

Fredrik Johansson, fredrik.johansson@chalmers.se
Computer Science and Engineering

Teaching assistant:

Lena Stempfle, stempfle@chalmers.se 
Computer Science and Engineering

Student representatives: 

  • Lisa Samuelsson
  • Philip Ivers Ohlsson
  • Junyu Sun
  • ...?

Announcements

  • Oct 17. Update to lecture notes. A typo has been corrected on page 70. p(Y(t)=y) -> E[Y(t)]
  • Oct 10. Feedback on project proposals. I made an announcement re: the visibility of feedback.
  • Oct 8. Assignment 3 has nowe been posted.
  • Oct 6. Update to lecture notes. I have added an early draft of Chapter 6: Policy evaluation. The lecture notes will not be completed all the way this year, but I thought anything may be useful. 
  • Oct 4. Live coding demo. Instructions for the demo have been added to the module page. The partially solved notebook is now uploaded. 
  • Sep 28. MSc thesis project portal. Some of you have asked me about finding thesis projects for the spring. These will be posted in the new portal: https://annonsportal.chalmers.se/ 
  • Sep 25. The consultation session on October 6 has been moved to October 9 (Monday 8:30-10:00, before the lecture)
  • Sep 20. For the consultation session on Sept 29, there will be no online option. Please come to my office if you want to make use of the office hours. 
  • Sep 18. Today's follow-up lecture will introduce the projects that you will do later in the course. Have a look at the slides if you can't attend.
  • Sep 16. More web resources:
  • Sep 13. Update to slides for Module 3. Notation has been updated to be consistent with lecture notes
  • Sep 11. Update to lecture notes. Corrected typos in the propensity adjustment thm, Theorem 4
  • Sep 5. Update to lecture notes. A typo has been corrected in the proof of Theorem 3. Y(1) -> Y(0)
  • Sep 1. Update to lecture notes. The lecture notes for Chapter 4 have been updated to include a statement on Parent adjustment. 
  • Aug 31. No change in lecture time. The Monday lectures will remain in the original time slot 10:00–11:45am. Based on the discussions at the Wednesday lecture and the responses to the Google form sent out on Aug 30, for every time slot on Mondays, there are several students who have lecture conflicts. For this reason, we will default to the original time that was advertised when you registered for the classes. 
  • Aug 28. The first lecture in the course is given today, Aug 28, in ES52 at 10am.
  • Aug 21. The lecture notes have been updated with more chapters, and the first 2 modules have been published on Canvas.
  • Aug 16. Have a look at the Preface of the course lecture notes to familiarize yourself with the goals of the course and necessary prerequisites. The notes are still an early draft, so constructive feedback is welcome.
  • Aug 16. The Canvas page has been created and the syllabus is being updated.

Schedule

Lectures: We meet in ES52 on Mondays (10:00-11:45am) Wednesdays (1:15–3:00pm).  Schedule: See TimeEdit

Recordings: Module Introduction lectures and non-interactive components of Follow-up sessions will be recorded and posted on the Canvas pages for each module. 

Consultation hours: Fridays 8:30–10:00am in EDIT 5482 and https://chalmers.zoom.us/j/62498373953, password: DAT465

Course purpose & structure

The main goal of this course is

 To establish conditions for and methods with which we can identify causality and improve decision-making by learning from data

The course should 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. Students who complete this course should be able to recognize, define, and solve problems related to causality. This will involve becoming familiar with and using several paradigms (languages) of causality as well as methods for causal estimation and inference.

The content is structured into 7 modules and a project, one topic 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 later 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. See the overview below.

 

Schedule-2.png

 

Sessions

  • Introduction lectures (Wednesdays Aug 30, Sep 6, 13, 20, 27, Mondays Oct 9, 16)
  • Follow-up lectures (Mondays Sep 4, 11, 18, 25, Oct 2, Wednesdays Oct 11, 18)
  • Live coding demo (October 5)

Modules

 The full schedule can be found in TimeEdit

Changes since 2022:

  • Lecture notes have been added to complement slides and external resources
  • The paper reviews assignment has been removed

Course literature

The course is based on public material such as research papers and e-books available online. It will not follow a textbook week by week, but each module will be accompanied by lecture notes, slides, live lectures and reading suggestions.

If you would like to read a textbook on the subject, consider one of the following:

  • Morgan, Stephen L., and Christopher Winship. Counterfactuals and causal inference. Cambridge University Press, 2015. 2nd Edition.
  • Pearl, Judea. Causality. Cambridge university press, 2009. Available online through the Chalmers library
  • Peters, Janzing & Schölkopf, Elements of Causal Inference, MIT Press, 2017. Available online through the Chalmers library
  • Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC. 

Additional resources: 

Learning objectives and syllabus

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

Knowledge and understanding

  • Provide an overview of 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 on the fundamental limitations to the possibility of causal identification
  • Critically analyze and discuss research and applications of causal inference, in particular with respect to adjustment for confounding factors

Link to the syllabus: Study plan (Chalmers), Studieplan (GU)

Examination

The examination in this course will include two components comprising a) written hand-in assignments, and b) an implementation project. These will take place throughout the entire course and are aimed to cover the full range of learning objectives. There will be no written final exam. Each problem-solving assignment will be awarded a score in the range 0–5p and the project a score in the range 0–10p. For problem-solving assignments, it is not necessary to attempt "advanced problems" to achieve a passing grade  (2-4p). Full marks (5p) cannot be achieved without attempting the advanced problem.

Assignments are to be solved individually. You are encouraged to discuss the material but to hand in your own solution. We expect a high degree of academic honesty in courses at this level. The project can be solved individually or in pairs, it's your choice. If you submit a solution on time that does not meet the requirements for a passing grade, you will get some feedback and be asked once to correct the most important errors within a stipulated period of time. If you miss the deadline (or the resubmission deadline), the assignment solution will get the grade of Fail (U). It will be possible to resubmit failed assignments only in the last week of the course. Resubmissions of failed assignments will be awarded the lowest passing grade.

The use of AI-based assistive tools, including but not limited to Chat-GPT, is permitted only for improving the language in written hand-ins. It is not permitted for solving the assignment problems or for generating discussion or analysis. Hopefully, you are taking this course to learn something, so make use of the opportunities you get. 

All instances of plagiarism, including but not limited to copying significant portions of text or code from the web will be reported and result in a failed assignment. 

For a passing final grade, each assignment should be completed with a minimum score of 40% (2p for assignments, 4p for project) and a total score, across all assignments, of 40% of the maximum number of points  (see grading thresholds below). To achieve a passing score, solutions are expected to have at most a few technical errors. To get a higher score, solutions/presentations should be correct, well-explained, and more insightful. Additionally, for the hand-in assignments, to achieve the highest score, the advanced problem must be solved.

Grading

  • For Chalmers and GU students (DAT465 / DIT654), a numerical scale is used (U, 3, 4, 5).

  • For PhD students, the grades for the course are  Pass (G)  or Fail (U)

The final grade will be based on the total sum of scores for all the assignments, using the following thresholds:

  • At least 80%  of the maximal: 5
  • At least 60–79%  of the maximal: 4
  • At least 40–59% of the maximal: 3

Course summary:

Course Summary
Date Details Due