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In this paper, we consider a risk-averse multi-armed bandit (MAB) problemwhere the goal is to learn a policy that minimizes the risk of low expectedreturn, as opposed to maximizing the expected return itself, which is theobjective in the usual approach to risk-neutral MAB. Specifically, we formulatethis problem as a transfer learning problem between an expert and a learneragent in the presence of contexts that are only observable by the expert butnot by the learner. Thus, such contexts are unobserved confounders (UCs) fromthe learner's perspective. Given a dataset generated by the expert thatexcludes the UCs, the goal for the learner is to identify the true minimum-riskarm with fewer online learning steps, while avoiding possible biased decisionsdue to the presence of UCs in the expert's data.

Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health