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The structural causal bandit (SCB) framework offers a graphical approach to identifying suboptimal actions by leveraging prior knowledge of the underlying causal structure. While theoretically appealing, there has been limited guidance on how to systematically transfer information across heterogeneous datasets from multiple environments. In this paper, we study the structural causal bandit under transportability, where prior knowledge from source environments is integrated to accelerate learning in a target deployment environment. We show that exploiting causal invariances across environments improves sample efficiency in identifying the optimal action.