Course syllabus

Welcome to Causality and Causal Inference!

DAT465 Causality and causal inference lp1 HT21 (7.5 hp)

Examiner & lecturer:

Fredrik Johansson,
Computer Science and Engineering

Teaching assistant:

Lena Stempfle, 

Computer Science and Engineering

Student representatives: 

  • Colton Cunov
  • Erik Jergéus 
  • Elias Sundqvist
  • Tommy Räjert

Schedule & Zoom instructions

We meet on Zoom on Mondays (10:00-12:00am) Wednesdays (1:15–3:00pm).  

Lectures will be recorded and posted in the module pages with the exception of in-class discussion sessions. Attend follow-up sessions live to take part in the discussion.

Meeting link:

Password: DAT465

Schedule: dat465.ics, Add to Google Calendar


  • Nov 10. I received a question about the "Paper selection" assignment having the status "Does not count toward the final grade". Note that this is only for the selection itself. I left the comments for your presentation there since there was no Canvas assignment for the presentation itself. According the syllabus, the presentation was always going to be assigned a grade 0–20. 
  • Nov 5. Grades for projects and presentations and the overall course grade will be sent out early next week. 
  • Oct 25. Recall that today's lecture is at 3pm
  • Oct 19. The paper presentation schedule is the following: 
Wednesday Monday
Tomas & Sara Gustav & Tommy
Yuchu & Koen Erik & Leo
Ivan Alexander
Wenhao Elias
David Avi
Bastian Colton
  • Oct 11. "Nobel prize" in economics to causality researchers!
  • Oct 11. The paper presentation session on October 25 has been moved to 3pm.
  • Oct 7. A new assignment for selecting papers for presentation in the final week has been posted. 
  • Oct 7. Assignment 3 has now been posted. For those without Canvas access, it is available as a file, and solutions can be submitted via email.
  • Oct 6. The consultation will take place on the standard Zoom link. 
  • Oct 4. You can now book a consultation slot for talking about your project proposals on Wednesday, Oct 6, in this spreadsheet
  • Sep 29. A solution to the exercise estimating ATE from the follow-up meeting for Obs studies P1 was posted. 
  • Sep 28. The recording from the introduction to projects and project proposals has been posted here.
  • Sep 27. The project proposal assignment has now been posted. More information will be given in today's follow-up lecture. All project information is collected in the project module page.
  • Sep 23. Assignment 2 has now been posted. For those without Canvas access, it is available as a file and a data file, and solutions can be submitted via email.
  • Sep 21. I delivered a seminar in the ML Seminar series at DSAI on the causality-related research in my lab. Have a look below if you are interested:
  • Sep 15. Thomas Lundberg shared the following announcement:
    Effective Altruism Chalmers is hosting a book club this fall on "The Book of Why" by Judea Pearl. Since we are taking a course on Causality, with many examples from "The Book of Why", some of you might be interested in joining. We will meet online (Zoom) four times in October and November, starting the 7th of October. It is an informal setting where we freely discuss a few chapters at every meeting (in English). The sessions are usually somewhere between 1-1.5 hours. Hopefully, this will be a nice compliment to the course. For those of you that are interested, feel free to attend the facebook event." 

  • Sep 14. The reading suggestions for module 3 have been updated. The video recording of the section on counterfactuals was uploaded to module 2.
  • Sep 9. Assignment 1 has now been posted. For those without Canvas access, it is available as a file and solutions can be submitted via email. 
  • Sep 6. The discussion function has now been enabled on Canvas. 
  • Sep 3. Student representative Colton Cunov shared a Discord group on the CAS MSc program server for this course: Feel free to use this for discussion.
  • Sep 1. The recordings from the lecture on Probability & Graphical models have now been posted
  • Aug 30. The recordings from the first lecture are now online
  • Aug 23. The course modules have been finalized and placeholder assignments with final dates have been posted. Make note of the fact that for the last module, live participation is required in the form of paper presentations.
  • Aug 10. The first lecture will be given on August 30 at 10:00am over Zoom.
  • July 7. Due to the ongoing COVID-19 pandemic, complying with Chalmers recommendations, the course will be given over Zoom. 
  • July 7. The first syllabus has been posted. More details will be posted shortly, including assignment deadlines. The examination components and grading scale is fixed and will not change. 

Course purpose & structure

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. 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 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 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-crop copy-4.png



  • Mondays are follow-up lectures
  • Wednesdays are introduction lectures


 The full schedule can be found in TimeEdit

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 slides, 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 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

Link to the syllabus on Studieportalen: Study plan


The examination in this course will include five components comprising a) three written hand-in assignments, b) a smaller implementation project, c) a presentation of a research papers. 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 completed assignment will be awarded a score in the range 0–20. 

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 before the deadline 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.

For a passing final grade, each assignment should be completed with a minimum score of 5 and a total score, across all assignments, of 40  (see grading thresholds below). To achieve a passing score (of 8), 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.


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

  • For GU students, the scale (U, G, VG) is used.

  • 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/VG
  • At least 60–79%  of the maximal: 4/G
  • At least 40–59% of the maximal: 3/G

Course summary:

Date Details Due