  Causal bandit is a nascent learning model where an agent sequentially
experiments in a causal network of variables, in order to identify the
reward-maximizing intervention. Despite the model's wide applicability,
existing analytical results are largely restricted to a parallel bandit version
where all variables are mutually independent. We introduce in this work the
hierarchical causal bandit model as a viable path towards understanding general
causal bandits with dependent variables. The core idea is to incorporate a
contextual variable that captures the interaction among all variables with
direct effects. Using this hierarchical framework, we derive sharp insights on
algorithmic design in causal bandits with dependent arms and obtain nearly
matching regret bounds in the case of a binary context.
