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Counterfactual Reasoning and Learning Systems

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arxiv 1209.2355 v5 pith:GWGYF5EP submitted 2012-09-11 cs.LG cs.AIcs.IRmath.STstat.TH

Counterfactual Reasoning and Learning Systems

classification cs.LG cs.AIcs.IRmath.STstat.TH
keywords systemschangeslearningsystemworkalgorithmsallowassociated
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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 changes that improve both the short-term and long-term performance of such systems. This work is illustrated by experiments carried out on the ad placement system associated with the Bing search engine.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Concrete Problems in AI Safety

    cs.AI 2016-06 accept novelty 7.0

    The paper categorizes five concrete AI safety problems arising from flawed objectives, costly evaluation, and learning dynamics.