Causal inference

Lecturer: Jaime Sevilla

Date: 04/11/2020

Time: 17:00 CET

Language: Spanish

Place: https://timeout.srcf.net/js2545-exb-neg-bsg (access code: 626408)

Abstract:

We will introduce the necessity of causal inference via Simpson’s paradox, the notion of causal graphical models as a way of representing probabilistic causal models, explain how to compute the effect of interventions using a graphical model, and overview the problems of causal discovery (learning the orientation of the edges of a causal model) and causal identifiability (measuring the strength of a causal link).

Bibliography

• Spirtes, Glymour, Scheines Causation, Prediction and Search.
• Pearl, Glymour, P. Jewell Causal Inference in Statistics: A Primer

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