December 16, 2013

Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising

Journal of Machine Learning Research (JMLR)

By: Denis Charles, Dipankar Ray, Ed Snelson, Elon Portugaly, Joaquin Quinonero Candela, Leon Bottou, Max Chickering, Patrice Simard

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.