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Challenges in causality: Results of the WCCI 2008 challenge| old_uid | 5250 |
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| title | Challenges in causality: Results of the WCCI 2008 challenge |
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| start_date | 2008/09/08 |
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| schedule | 16h30 |
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| online | no |
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| summary | What affects your health, the economy, climate changes? And what actions will have beneficial effects? These are some of the central questions of causal discovery. A "causal model" is a model capable of making predictions under changing circumstances, corresponding to actions of "external agents" on a system of interest. For example, a doctor administering a drug to a patient, a government enforcing a new tax law or a new environmental policy. It is often necessary to assess the benefits and risks of potential actions using available past data and excluding the possibility of experimenting. Experiments, which are the ultimate way of verifying causal relationships, are in many cases too costly, infeasible, or unethical. For instance, enforcing a law prohibiting to smoke in public places is costly, preventing people from smoking may be infeasible, and forcing them to smoke would be unethical. In contrast, "observational data" are available in abundance in many applications. Recently, methods to devise causal models from observational data have been proposed. Can causal models thus obtained be relied upon to make important decisions? In this presentation, we will review the results of a challenge we have recently organized, which attracted over 50 teams worldwide. The result indicate that causal discovery from observational data is not an impossible task, but a very hard one. This points to the need for further research and benchmarking.
Want to play? Check the "causality workbench" at http://clopinet.com/causality |
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| responsibles | Bourgine |
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