Before revision:  We leverage parametric and non-parametric statistical estimation methods and causal effect estimation methods in order to propose new contextual bandit designs. Through a variety of simulations, we show how alternative design choices impact the learning performance and provide insights on why we observe these effects .
After revision:  We leverage parametric and non-parametric statistical estimation methods and causal effect estimation methods in order to reduce the estimation bias that results from adaptive treatment assignment. We provide empirical evidence that guides the choice among the alternatives in different scenarios, such as prejudice (non-representative user contexts) in the initial training data .