Research
Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising
December 16, 2013
Abstract
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.
AUTHORS
Written by
Jonas Peters
Denis Charles
Max Chickering
Elon Portugaly
Dipankar Ray
Patrice Simard
Ed Snelson
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