  We study the piecewise stationary combinatorial semi-bandit problem with
causally related rewards. In our nonstationary environment, variations in the
base arms' distributions, causal relationships between rewards, or both, change
the reward generation process. In such an environment, an optimal
decision-maker must follow both sources of change and adapt accordingly. The
problem becomes aggravated in the combinatorial semi-bandit setting, where the
decision-maker only observes the outcome of the selected bundle of arms. The
core of our proposed policy is the Upper Confidence Bound (UCB) algorithm. We
assume the agent relies on an adaptive approach to overcome the challenge. More
specifically, it employs a change-point detector based on the Generalized
Likelihood Ratio (GLR) test. Besides, we introduce the notion of group restart
as a new alternative restarting strategy in the decision making process in
structured environments. Finally, our algorithm integrates a mechanism to trace
the variations of the underlying graph structure, which captures the causal
relationships between the rewards in the bandit setting. Theoretically, we
establish a regret upper bound that reflects the effects of the number of
structural- and distribution changes on the performance. The outcome of our
numerical experiments in real-world scenarios exhibits applicability and
superior performance of our proposal compared to the state-of-the-art
benchmarks.
