  Sequential decision-making under uncertainty is often associated with long
feedback delays. Such delays degrade the performance of the learning agent in
identifying a subset of arms with the optimal collective reward in the long
run. This problem becomes significantly challenging in a non-stationary
environment with structural dependencies amongst the reward distributions
associated with the arms. Therefore, besides adapting to delays and
environmental changes, learning the causal relations alleviates the adverse
effects of feedback delay on the decision-making process. We formalize the
described setting as a non-stationary and delayed combinatorial semi-bandit
problem with causally related rewards. We model the causal relations by a
directed graph in a stationary structural equation model. The agent maximizes
the long-term average payoff, defined as a linear function of the base arms'
rewards. We develop a policy that learns the structural dependencies from
delayed feedback and utilizes that to optimize the decision-making while
adapting to drifts. We prove a regret bound for the performance of the proposed
algorithm. Besides, we evaluate our method via numerical analysis using
synthetic and real-world datasets to detect the regions that contribute the
most to the spread of Covid-19 in Italy.
