Balanced Linear Contextual Bandits [article]

Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey, Guido Imbens
2018 arXiv   pre-print
Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We develop algorithms for contextual bandits with linear payoffs that integrate balancing methods from the causal inference literature in their estimation to make it less prone to problems of estimation bias. We
more » ... de the first regret bound analyses for linear contextual bandits with balancing and show that our algorithms match the state of the art theoretical guarantees. We demonstrate the strong practical advantage of balanced contextual bandits on a large number of supervised learning datasets and on a synthetic example that simulates model misspecification and prejudice in the initial training data.
arXiv:1812.06227v1 fatcat:fjvhmzl3kzb3zfpehdz65aipzu