Causal Bandit Over Unknown Graphs: Upper Confidence Bounds With Backdoor Adjustment
未知因果图上的因果老虎机：结合后门调整的置信上界AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要以因果图、后门调整和序贯干预效应估计为核心，直接属于因果推断。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
未知因果图上的因果老虎机：结合后门调整的置信上界
- 研究问题
- 因果图未知时，如何结合序贯获得的观测与实验数据选择干预，以最大化期望奖励并降低累积遗憾？
- 方法与思路
- 作者识别候选后门调整集，融合两类数据估计干预效应及置信上界，提出 BA-UCB，并扩展至含潜在混杂的混合图。
- 材料陈述的贡献
- 摘要称建立有限时间遗憾上界，相对标准老虎机减弱对干预臂数量的依赖，模拟显示更低遗憾与较好的计算效率。
- 阅读关注点
- 应核对候选调整集如何保证有效、图结构学习误差如何进入置信界，以及潜在混杂扩展所需条件和遗憾比较范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未列出全部图结构与识别条件，改善不能推广到任意未知因果系统。
原文摘要
The causal bandit problem seeks to identify, through sequential experimentation, an intervention that maximizes the expected reward in a causal system modeled by a directed acyclic graph (DAG). Existing methods typically assume that the causal graph is known or impose restrictive structural assumptions. In this paper, we study causal bandit problems when the causal graph is unknown. We first consider Gaussian DAG models without latent confounders. By combining observational and experimental data collected sequentially during the bandit process, we identify candidate backdoor adjustment sets for each intervention arm. These sets enable estimation of causal effects and construction of upper confidence bounds that integrate information from both data sources. Based on these estimates, we propose a new algorithm, termed backdoor-adjustment upper confidence bound (BA-UCB), for sequential intervention selection. We establish finite-time upper bounds on the cumulative regret of BA-UCB, showing improved rates and substantially relaxed dependency on the number of intervention arms compared to standard multi-armed bandit methods. We further extend the methodology and theoretical guarantees to settings with latent confounders, where the observed variables are modeled by an acyclic directed mixed graph. Simulation studies demonstrate that BA-UCB achieves substantially lower cumulative regret and favorable computational efficiency relative to existing approaches.