Intro in structural learning and causal discovery (rus)
Date:
What can data tell us about causality? Speaker introduces an introduction to structured learning and causal discovery. He examines the intuition behind graphical probabilistic models, and the trade-off between the number of assumptions and the robustness of inferences, using the examples of constrain-based (PC, FCI), scoring-based (GIES) and other (LiNGAM) basic approaches implemented in the “Causal Discovery Toolbox” and “Causal-learn” libraries. (Presentation language rus)