Counterfactual Reasoning and Learning Systems

09/11/2012

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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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Counterfactual Reasoning and Learning Systems

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Counterfactual Reasoning and Learning Systems

Related Research

Generalized Kernel Ridge Regression for Long Term Causal Inference: Treatment Effects, Dose Responses, and Counterfactual Distributions

Causal Inference in the Presence of Interference in Sponsored Search Advertising

Persistence in Complex Systems

Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution

Adaptable and Verifiable BDI Reasoning

Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links

Exploiting the Short-term to Long-term Plasticity Transition in Memristive Nanodevice Learning Architectures