Course Syllabus for

Introduction to Causal Inference in Time Series
Introduktion till kausal inferens i tidsserier

FRT295F, 7.5 credits

Valid from: Spring 2023
Decided by: Maria Sandsten
Date of establishment: 2023-03-16

General Information

Division: Automatic Control
Course type: Third-cycle course
Teaching language: English


The goal is to 1) provide students with an overview of modern causal inference methods, and 2) provide them with knowledge of when and how to apply such methods.


Knowledge and Understanding

For a passing grade the doctoral student must

Competences and Skills

For a passing grade the doctoral student must

Judgement and Approach

For a passing grade the doctoral student must demonstrate understanding of the differences between classical statistical methods and causal inference methods

Course Contents

Topics include: - foundations of causal inference (defining causal models and causal effects, observational and interventional data, connections to experiments/randomized trials) - identifiability and estimation of causal effects - graphs as representations of causal systems - causal inference in time series models and connections to control theory

Course Literature

Jonas Peters, D. & Schölkopf, B.: Elements of Causal Inference. The MIT Press, 2017. ISBN 9780262037310.
Open access e-book is available online.

Instruction Details

Types of instruction: Lectures, exercises

Examination Details

Examination format: Written assignments
Grading scale: Failed, pass

Admission Details

Assumed prior knowledge: basic statistics, probability theory, and linear algebra

Course Occasion Information

Contact and Other Information

Course coordinator: Søren Wengel Mogensen <>

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