English

If sufficient demand

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.

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

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

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

demonstrate understanding of the differences between classical statistical methods and causal inference methods

Lectures

Exercises

Written assignments

Failed, pass

basic statistics, probability theory, and linear algebra

Jonas Peters, D. & Schölkopf, B.: Elements of Causal Inference. The MIT Press, 2017. ISBN 9780262037310.

Open access e-book is available online.

FRT295F

2023-03-16

Maria Sandsten