Balanced Linear Contextual Bandits


  • Maria Dimakopoulou Stanford University
  • Zhengyuan Zhou Stanford University
  • Susan Athey Stanford University
  • Guido Imbens Stanford University



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 provide 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.




How to Cite

Dimakopoulou, M., Zhou, Z., Athey, S., & Imbens, G. (2019). Balanced Linear Contextual Bandits. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 3445-3453.



AAAI Technical Track: Machine Learning