Causal Bandits: Learning Good Interventions via Causal Inference
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...[Show more]
|Collections||ANU Research Publications|
|Source:||Advances in Neural Information Processing Systems 29: 30th Annual Conference on Neural Information Processing Systems 2016|
|01_Lattimore_Causal_Bandits%3A_Learning_Good_2016.pdf||1.5 MB||Adobe PDF||Request a copy|
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