Recast contextual bandit problem as causal inference

I have a contextual bandit problem with (S - state, A - action, R - reward) where S is high-dimensional vector, A is continuous value, R is continuous value, how do I learn optimal mapping function from state to action to maximize reward? It seems the current package can only estimate continuous treatment effect given a control treatment, but O don't have a control treatment. Furthermore, all current estimators don't scale to large data and high dimensional (millions of samples in train / test set, thousands of dimensions). Is there a way to make it more scalable?

The issue names no file, test, or entry point. Start by reviewing the existing continuous-treatment effect estimators and their assumptions, then determine whether contextual bandits, continuous actions, high-dimensional data, and scalability require a new design; completion criteria are not defined in the report.