Causal contextual bandits with one-shot data integration.

ABSTRACT: We study a contextual bandit setting where the agent has access to causal side information, in addition to the ability to perform multiple targeted experiments corresponding to potentially different context-action pairs-simultaneously in one-shot within a budget. This new formalism provides a natural model for several real-world scenarios where parallel targeted experiments can be conducted and where some domain knowledge of causal relationships is available. We propose a new algorithm that utilizes a novel entropy-like measure that we introduce. We perform several experiments, both using purely synthetic data and using a real-world dataset. In addition, we study sensitivity of our algorithm's performance to various aspects of the problem setting. The results show that our algorithm perfor

SUBMITTER: Subramanian C 

PROVIDER: S-EPMC11659213 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature