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WSC '87 Proceedings of the 19th conference on Winter simulation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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Caveats for causal reasoning with equilibrium models
Caveats for causal reasoning with equilibrium models
Cybernetics, Second Edition: or the Control and Communication in the Animal and the Machine
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Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
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ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
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ECML'05 Proceedings of the 16th European conference on Machine Learning
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WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
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This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such predictions allow both humans and algorithms to select the changes that would have improved the system performance. This work is illustrated by experiments on the ad placement system associated with the Bing search engine.