University of Cambridge > Talks.cam > Machine Learning Journal Club > Correlation, Causation, Model Comparison and Interventions

Log in

University Account

External (via Google)

Information on

Subscribing to talks details

Finding a talk details

Adding a talk details

Disseminating talks details

Help and Documentation details

Correlation, Causation, Model Comparison and Interventions

Download to your calendar using vCal

Philipp Hennig (University of Cambridge)
Friday 21 August 2009, 11:30-12:30
TCM Seminar Room, Cavendish Laboratory, Department of Physics.

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.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Correlation, Causation, Model Comparison and Interventions

📅 Download to calendar (vCal)

👤 Speaker: Philipp Hennig (University of Cambridge)
📅 Date & Time: Friday 21 August 2009, 11:30 - 12:30
📍 Venue: TCM Seminar Room, Cavendish Laboratory, Department of Physics

Questions? Contact Emli-Mari Nel

Abstract

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)

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

Included in Lists

Note: Ex-directory lists are not shown.

Log in

🔐 Log In

Information on

ℹ️ Information

Correlation, Causation, Model Comparison and Interventions

This talk is included in these lists:

Correlation, Causation, Model Comparison and Interventions

Abstract

Included in Lists

Log in

🔐 Log In

Information on

ℹ️ Information

Correlation, Causation, Model Comparison and Interventions

This talk is included in these lists:

Other lists

Other talks

Correlation, Causation, Model Comparison and Interventions

Abstract

Included in Lists