Grigory Chernov's report for Open data science

In June 2022, laboratory employee Grigory Chernov presented the report Introduction to structured learning and causal discovery.

Report by Grigory Chernov, economist, PhD student at the Laboratory of Experimental and Behavioral Economics at the Higher School of Economics, visiting researcher at the Department of Decision-Making at the University of Tübingen. What can the data tell us about causality? Grigory introduces the introduction to structured learning and causal discovery. The report examines the intuition behind graphical probabilistic models, as well as the possibility of a compromise between the number of assumptions and the stability of conclusions using the example of constraint-based (PC, FCI), scoring based (GIES) and other (LiNGAM) basic approaches implemented in the Causal Discovery Toolbox and Causal-learn libraries.
The recording of the report is available at the link: Grigory Chernov | Intro in structural learning and casual discovery - YouTube