Title
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.
Year
DOI
Venue
2013
10.5555/2567709.2567766
Journal of Machine Learning Research
Keywords
Field
DocType
ad placement system,causal inference,system performance,computational advertising,counterfactual reasoning,bing search engine,complex learning system,causation
Causal inference,Search engine,Leverage (finance),Computer science,Computational advertising,Causation,Counterfactual thinking,Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
14
Issue-in-Progress
1532-4435
Citations 
PageRank 
References 
106
4.33
23
Authors
9
Search Limit
100106
Name
Order
Citations
PageRank
Léon Bottou1117541364.56
Jonas Peters250531.25
Joaquin Quiñonero-Candela368938.37
Denis X. Charles41386.27
D. Max Chickering51064.33
Elon Portugaly628625.89
Dipankar Ray71195.82
Patrice Simard81268621.43
Ed Snelson91154.99