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Correlation, Causation, Model Comparison and Interventions

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If you have a question about this talk, please contact Emli-Mari Nel.

In this week’s journal club, I would like to discuss the much-touted but rarely properly discussed relationship between correlation and causation. In particular, what is the precise difference between the two, and what role does active intervention (experimentation) play?

Much of the academic discussion on this topic has taken the form of big books. However, a concise paper dealing with some aspects of these questions is

Active Learning for Structure in Bayesian Networks by Simon Ton and Daphne Koller, ICML 2001.

For a more fun introduction to some of the problems, involving mind-reading aliens(!), see the first few pages of

Inferring causal networks from observations and interventions by Steyvers et al., Cognitive Science 27 (2003) 453-489)

This talk is part of the Machine Learning Journal Club series.

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