Causal inference  (3 cr)

June 9-13, 2025, University of Tampere

Lecturer: Professor Juha Karvanen, University of Jyväskylä

Information about the course will be updated, so stay tuned!

Course description

The course starts from the key concepts of causality, focuses on the identification and estimation of causal effects, and gives an overview of different areas of causal inference.  20 hours of teaching consists of lectures and exercises. R is used in examples. The course is completed with an exam. The topics include:

  • causality in science, decision making and everyday life
  • difference of prediction and causal inference, confounders, ladders of causality
  • graphical framework: structural causal model, directed acyclic graph (DAG), directed acyclic mixed graph (ADMG)
  • potential outcome framework
  • d-separation and conditional independence
  • identifiability of causal effects
  • do-calculus and ID algorithm
  • estimation of causal effects: g formula, inverse probability weighting (IPW), Bayesian
  • causal discovery
  • counterfactual inference
  • causal data fusion
  • graphical missing data models
     

Learning outcomes


A student who has completed the course

  • knows the concept of causality,
  • knows the potential outcome framework and graphical framework for causal inference,
  • can represent causal relations with graphical models,
  • can apply do-calculus and the ID algorithm,
  • can estimate causal effects from observational data,
  • knows the basics of causal discovery and counterfactual inference,
  • knows the basics of causal data fusion and graphical missing data models

 

Prerequisites


Basic probability (random variables, conditional independence), basic discrete mathematics (sets and graphs), understanding of statistical modeling (e.g. generalized linear models), basics of algorithms, familiarity with R.

Literature


The course does not directly follow any book but the most of the material can be found from these books:

  • Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: a primer. John Wiley & Sons.
  • Pearl, J. (2009). Causality: Models, Reasoning, and Inference (Second ed.). Cambridge University Press.
  • Hernán, M. A & Robins, J.M. (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC. https://miguelhernan.org/whatifbook
  • Peters, J., Janzing, D. & Schölkopf, B. (2017) Elements of Causal Inference. MIT Press.
  • Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press.