Title: On Transportability for Structural Causal Bandits
Primary area: causal reasoning
Keywords: causal inference, identification, transportability, structural causal bandits, causal decision making

Abstract: Intelligent agents equipped with causal knowledge can optimize their action spaces to avoid unnecessary exploration. The *structural causal bandit* framework provides a graphical characterization for identifying actions that are unable to maximize rewards by leveraging prior knowledge of the underlying causal structure. While such knowledge enables an agent to estimate the expected rewards of certain actions based on others in online interactions, there has been little guidance on how to transfer information inferred from arbitrary combinations of datasets collected under different conditions---observational or experimental---and from heterogeneous environments. In this paper, we investigate the structural causal bandit with *transportability*, where priors from the source environments are fused to enhance learning in the deployment setting. We demonstrate that it is possible to exploit invariances across environments to consistently improve learning. The resulting bandit algorithm achieves a sub-linear regret bound with an explicit dependence on informativeness of prior data, and it may outperform standard bandit approaches that rely solely on online learning.

TL;DR: We study a structural causal bandit framework that leverages prior data while accounting for structural discrepancies across environments