# Balanced Linear Contextual Bandits Source: [Balanced Linear Contextual Bandits]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:20:52.699Z Authors: Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey, Guido Imbens Published: 2018-12-15T03:06:51.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Coupon Recommendation on commercial product](): Coupon Recommendation; commercial product; CVR: 86.63. ## Original sources and implementation [Original paper]() [PDF]()