Before revision:  We leverage parametric and non-parametric statistical estimation methods and causal effect estimation methods in order to propose new contextual bandit designs.
After revision:  We show that 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 propose new contextual bandit designs, combining parametric and non-parametric statistical estimation methods and causal effect estimation methods in order to propose new contextual bandit designs.