This presentation will introduce the basics of causal modeling and explain how it differs from probabilistic modeling. Using Judea Pearl’s ladder of causality, we will show how causal models go beyond identifying associations (level 1) to reasoning about interventions (level 2) and counterfactuals (level 3). We will cover structural causal models and causal diagrams like directed acyclic graphs (DAGs), which help answer “what-if” questions about variables in a system. We will also discuss ways to build causal models, including using domain knowledge and methods that learn from observational data, such as score-based, constraint-based, and functional causal approaches. To make these ideas concrete, we will provide an example using the LiNGAM (Linear Non-Gaussian Acyclic Model) and share some insights from our recent research on this model.

Speaker

Sarah Leyder is a PhD candidate in UAntwerp.

Time and Place

Wednesday 19/03/2025 at 13:45pm in M.A.143

Registration

Participation is free, but registration is compulsory. Make sure to fill in …

References and Related Reading

A general reference:

  • Matthew J Vowels, Necati Cihan Camgoz, and Richard Bowden. D’ya like DAGs? a survey on structure learning and causal discovery. ACM Computing Surveys, 55(4):1–36, 2022.
  • Shohei Shimizu. LiNGAM: Non-gaussian methods for estimating causal structures. Behaviormetrika, 41(1):65–98, 2014. Sarah Leyder, Jakob Raymaekers, and Tim Verdonck. TSLiNGAM: Directlingam under heavy tails. Journal of Computational and Graphical Statistics, 2024. A fun read:
  • Judea Pearl and Dana Mackenzie. The Book of Why: The New Science of Cause and Effect. 2018.