Combinatorial Causal Bandits

@article{Feng2022CombinatorialCB,
  title={Combinatorial Causal Bandits},
  author={Shi Feng and W. Chen},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.01995}
}
In combinatorial causal bandits (CCB), the learning agent chooses at most K variables in each round to intervene, col-lects feedback from the observed variables, with the goal of minimizing expected regret on the target variable Y . Different from all prior studies on causal bandits, CCB needs to deal with exponentially large action space. We study under the context of binary generalized linear models (BGLMs) with a succinct parametric representation of the causal models. We present the… 

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