Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combining Offline Causal Inference and Online Bandit Learning for Data
  Driven Decision   A fundamental question for companies with large amount of logged data is: How
to use such logged data together with incoming streaming data to make good
decisions? Many companies currently make decisions via online A/B tests, but
wrong decisions during testing hurt users' experiences and cause irreversible
damage. A typical alternative is offline causal inference, which analyzes
logged data alone to make decisions. However, these decisions are not adaptive
to the new incoming data, and so a wrong decision will continuously hurt users'
experiences. To overcome the aforementioned limitations, we propose a framework
to unify offline causal inference algorithms (e.g., weighting, matching) and
online learning algorithms (e.g., UCB, LinUCB). We propose novel algorithms and
derive bounds on the decision accuracy via the notion of "regret". We derive
the first upper regret bound for forest-based online bandit algorithms.
Experiments on two real datasets show that our algorithms outperform other
algorithms that use only logged data or online feedbacks, or algorithms that do
not use the data properly.

21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
2
The categories of this paper are: math
10
The categories of this paper are: q-bio