Counterfactual reasoning and learning systems: the example of computational advertising
Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela, Denis Xavier Charles, David M. Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, Ed Snelson · 2013
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 sys-tem. 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.