Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising
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.
Cite
Text
Bottou et al. "Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising." Journal of Machine Learning Research, 2013.Markdown
[Bottou et al. "Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising." Journal of Machine Learning Research, 2013.](https://mlanthology.org/jmlr/2013/bottou2013jmlr-counterfactual/)BibTeX
@article{bottou2013jmlr-counterfactual,
title = {{Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising}},
author = {Bottou, Léon and Peters, Jonas and Quiñonero-Candela, Joaquin and Charles, Denis X. and Chickering, D. Max and Portugaly, Elon and Ray, Dipankar and Simard, Patrice and Snelson, Ed},
journal = {Journal of Machine Learning Research},
year = {2013},
pages = {3207-3260},
volume = {14},
url = {https://mlanthology.org/jmlr/2013/bottou2013jmlr-counterfactual/}
}