Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising

L. Bottou; Jonas Martin Peters; J. Quiñonero-Candela; D. X. Charles; D. M. Chickering; E. Portugualy; D. Ray; P. Simard; E. Snelson

Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising

L. Bottou, Jonas Martin Peters, J. Quiñonero-Candela, D. X. Charles, D. M. Chickering, E. Portugualy, D. Ray, P. Simard, E. Snelson

178 Citations (Scopus)

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.

Original language	Undefined/Unknown
Journal	Journal of Machine Learning Research
Volume	14
Pages (from-to)	3207-3260
Number of pages	54
ISSN	1533-7928
Publication status	Published - Nov 2013
Externally published	Yes

Cite this

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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.",

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AU - Quiñonero-Candela, J.

AU - Charles, D. X.

AU - Chickering, D. M.

AU - Portugualy, E.

AU - Ray, D.

AU - Simard, P.

AU - Snelson, E.

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AB - 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.

M3 - Tidsskriftartikel

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VL - 14

SP - 3207

EP - 3260

JO - Journal of Machine Learning Research

JF - Journal of Machine Learning Research

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