Causal Bandits: Learning Good Interventions via Causal Inference
Date
2016
Authors
Lattimore, Finnian Rachel
Lattimore, Tor
Reid, Mark
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Neural Information Processing Systems Foundation
Abstract
We study the problem of using causal models to improve the rate at which good interventions can be learned online in a stochastic environment. Our formalism combines multi-arm bandits and causal inference to model a novel type of bandit feedback that is not exploited by existing approaches. We propose a new algorithm that exploits the causal feedback and prove a bound on its simple regret that is strictly better (in all quantities) than algorithms that do not use the additional causal information
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Advances in Neural Information Processing Systems 29: 30th Annual Conference on Neural Information Processing Systems 2016
Type
Conference paper
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Restricted until
2037-12-31