Combinatorial Causal Bandits. (arXiv:2206.01995v3 [cs.LG] UPDATED)

In combinatorial causal bandits (CCB), the learning agent chooses at most $K$
variables in each round to intervene, collects 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 algorithm BGLM-OFU for Markovian …

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