  In this paper, we consider a risk-averse multi-armed bandit (MAB) problem
where the goal is to learn a policy that minimizes the risk of low expected
return, as opposed to maximizing the expected return itself, which is the
objective in the usual approach to risk-neutral MAB. Specifically, we formulate
this problem as a transfer learning problem between an expert and a learner
agent in the presence of contexts that are only observable by the expert but
not by the learner. Thus, such contexts are unobserved confounders (UCs) from
the learner's perspective. Given a dataset generated by the expert that
excludes the UCs, the goal for the learner is to identify the true minimum-risk
arm with fewer online learning steps, while avoiding possible biased decisions
due to the presence of UCs in the expert's data.
