Before revision: Although many contextual bandit algorithms have similar theoretical guarantees, the characteristics of real-world applications oftentimes result in large performance dissimilarities across algorithms .
After revision: 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 .