发现因果老虎机中学习因果图反而是低效的，无需找父节点也能最优决策。
Graph Learning Is Suboptimal in Causal Bandits
- 不依赖因果图恢复，直接设计新算法优化后悔值。
- 证明了最小化后悔与识别父节点目标本质冲突，存在理论下界。
- 实验显示新方法显著优于现有基线，尤其在复杂环境。
我们在因果充分性假设下研究因果老虎机中的后悔最小化问题，其中智能体未知底层因果结构。以往工作聚焦于识别奖励变量的父节点，随后对这些父节点应用经典老虎机方法，或同时学习父节点并最小化后悔。我们探究此类策略是否最优。出人意料的是，我们的结果表明学习父节点集是次优的。通过证明存在实例使得后悔最小化与父节点识别为根本冲突的目标，我们进一步分析了已知与未知父节点数量的情形，建立了捕捉动作空间组合结构的新后悔下界。基于这些洞察，我们提出了近似最优的算法，绕过图结构和父节点恢复，证明父节点识别对后悔最小化并非必要。实验验证了在多种环境中，我们的方法与现有基线之间存在显著性能差距。
原文摘要 · Abstract (English)
We study regret minimization in causal bandits under causal sufficiency where the underlying causal structure is not known to the agent. Previous work has focused on identifying the reward's parents and then applying classic bandit methods to them, or jointly learning the parents while minimizing regret. We investigate whether such strategies are optimal. Somewhat counterintuitively, our results show that learning the parent set is suboptimal. We do so by proving that there exist instances where regret minimization and parent identification are fundamentally conflicting objectives. We further analyze both the known and unknown parent set size regimes, establish novel regret lower bounds that capture the combinatorial structure of the action space. Building on these insights, we propose nearly optimal algorithms that bypass graph and parent recovery, demonstrating that parent identification is indeed unnecessary for regret minimization. Experiments confirm that there exists a large performance gap between our method and existing baselines in various environments.
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