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

## Aim

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.

## Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• demonstrate understanding of the causal modeling formalism introduced in the course
• demonstrate knowledge of the differences between observational and interventional data
• demonstrate knowledge of the connection between experiments and causal models
• demonstrate knowledge of the connections between causal modeling and control theory
• demonstrate understanding of causal modeling in time series and how it differs from other types of causal modeling

Competences and Skills

For a passing grade the doctoral student must

• be able to apply the causal identification methods introduced in the course
• be able to apply the graphical methods introduced in the course
• be able to solve applied causal inference problems studied in the course

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
Examiner:

## Admission Details

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

## Contact and Other Information

Course coordinator: Søren Wengel Mogensen <soren.wengel_mogensen@control.lth.se>