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A fundamental question for companies with large amount of logged data is: Howto use such logged data together with incoming streaming data to make gooddecisions? Many companies currently make decisions via online A/B tests, butwrong decisions during testing hurt users' experiences and cause irreversibledamage. A typical alternative is offline causal inference, which analyzeslogged data alone to make decisions. However, these decisions are not adaptiveto the new incoming data, and so a wrong decision will continuously hurt users'experiences. To overcome the aforementioned limitations, we propose a frameworkto unify offline causal inference algorithms (e.g., weighting, matching) andonline learning algorithms (e.g., UCB, LinUCB). We propose novel algorithms andderive bounds on the decision accuracy via the notion of "regret". We derivethe first upper regret bound for forest-based online bandit algorithms.Experiments on two real datasets show that our algorithms outperform otheralgorithms that use only logged data or online feedbacks, or algorithms that donot use the data properly.

Combining Offline Causal Inference and Online Bandit Learning for Data Driven Decision