Course

MGT-416: Causal inference

Summary

Students will learn the core concepts and techniques of network analysis with emphasis on causal inference. Theory and application will be balanced, with students working directly with network data throughout the course.

Lectures

Instructors

Official source

https://edu.epfl.ch/coursebook/en/causal-inference-MGT-416

Moodle Page

http://go.epfl.ch/MGT-416

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Summary

Lectures

Instructors

Official source

https://edu.epfl.ch/coursebook/en/causal-inference-MGT-416

Moodle Page

http://go.epfl.ch/MGT-416

About this result

Lectures in this course (16)

Learning Latent Models in Graphical Structures

Explores learning latent models in graphical structures, focusing on scenarios with incomplete samples and introducing the notion of distance among variables.

Latent Tree Learning

Explores latent tree learning, covering node properties, sibling relationships, and algorithmic structure.

Pearl's Do Calculus: Rules and Intuition

Explains Pearl's do calculus rules and intuition, including action/observation exchange and interventions.

Causal Inference: Learning Graph Structures

Explores causal inference through learning graph structures for causal reasoning from observational data.

Edge Elimination and Orientation in Graphs

Covers edge elimination and orientation in graphs, including first order tests and logical orientation.

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