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2013 JMLR JMLR 2013

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. [abs] [ pdf ][ bib ] © JMLR 2013. (edit, beta)

🌉 Interdisciplinary Bridge — Artificial Intelligence and Knowledge & Reasoning
📈 Trend Setter — Causal Inference
🧭 Keyword Pioneer — counterfactual reasoning
🐣 Hot Topic Early Bird — causal inference
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio

Authors

Léon Bottou , Jonas Peters , Joaquin Quiñonero-Candela , Denis X. Charles , D. Max Chickering , Elon Portugaly , Dipankar Ray , Patrice Simard , Ed Snelson

Topics

Artificial Intelligence > Core AI > Causal Inference Knowledge & Reasoning > Reasoning > Causal Inference Machine Learning > Learning Types > Reinforcement Learning

Keywords

causal inference online learning counterfactual reasoning computational advertising learning system ad placement
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