Topic outline

  • General

    • Responsible teacher: Pekka Marttinen

      Teaching assistant: Tommi Gröhn

      Contact: by default please use the Slack ( https://join.slack.com/t/elementsofcau-jco2607/shared_invite/zt-1fso6kv3k-uWQkX15geM0S7bpyROB3ng ). In case you cannot use Slack for some reason, contact: tommi.i.grohn@aalto.fi

      Periods I-II

      Description: We will read the book "Elements of Causal Inference: Foundations and Learning Algorithms" by Peters et al. (https://mitpress.mit.edu/books/elements-causal-inference) and meet once a week to discuss what we have read. The course is organized only for PhD students this year.

      Format:

      For 3 credits:
      -Reading the book (approximately 200 pages)
      -Weekly meetings to discuss (10 times 1 hour)
      -A small writing task to prepare before each meeting (writing down the main points, questions, and comments of the section read)
      For 5 credits:
      -In addition to the above, solving selected problems from the book and attending exercise sessions (5 times 2 hours). Before the exercise session each student returns his/her solutions with the idea that he/she is willing to present the solutions to other students.

      The course is organized live (the room will still be announced). As we are a small group, we hope that all meetings are live but a hybrid option is also possible. The meetings will take place on Mondays from 2 to 3 pm, and the first meeting is on Monday, Sep 12th. More information about meeting times can be found on the "Lectures" page and more information about the exercise sessions on the "Assignments" page. Please prepare already for the first meeting by reading the book and completing the preparatory writing task (see details in Lectures and Writing tasks).

      Grading pass/fail. Passing requires the whole book to be read, all preparatory writing tasks completed, and actively participating in at least 9 face-to-face meetings (missing more meetings requires e.g. a doctor's note). For 5 credits, at least 70% of the assignments must be returned (and demonstrate a fair attempt to solve the problem) and the student must be available to present the solutions of his/her returned assignments in exercise sessions.