arXiv AI July 7, 2026 By Harsh Shrivastava, Yuta Kawakami, Junpei Komiyama, Jin Tian

Fixed-Confidence Best-Arm Identification for Causal Mediation Analysis

arXiv:2607. 04315v1 Announce Type: cross Abstract: This paper studies the problem of identifying the treatment that maximizes the expected natural direct potential outcome (NDPO), which captures the potential outcome of an intervention while excluding the pathway transmitted through a mediator that researchers may wish to remove from evaluation.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

reinforcement-learning

arXiv AI

Jul 20

Information-Directed Sampling for Causal Bandits

arXiv:2607. 15577v1 Announce Type: cross Abstract: Causal bandits exploit structural relationships among variables to share information across interventions and accelerate the identification of high-reward decisions.

By Muhammad Qasim Elahi, Murat Kocaoglu, Mahsa Ghasemi

reinforcement-learning

arXiv AI

Jun 29

The Minimal Search Space for Conditional Causal Bandits

arXiv:2502. 06577v3 Announce Type: replace-cross Abstract: Causal knowledge can be used to support decision-making problems.

By Francisco N. F. Q. Simoes, Itai Feigenbaum, Mehdi Dastani, Thijs van Ommen

reinforcement-learning

arXiv Machine Learning

Jun 16

Best Arm Identification with Minimal Regret

arXiv:2409. 18909v2 Announce Type: replace Abstract: Motivated by real-world applications that necessitate responsible experimentation, we introduce the problem of best arm identification (BAI) with minimal regret.

By Junwen Yang, Vincent Y. F. Tan, Tianyuan Jin

agents reinforcement-learning

arXiv Machine Learning

Jun 2

Evaluating and Learning Robust Bandit Policies Under Uncertain Causal Mechanisms

arXiv:2508. 02812v3 Announce Type: replace Abstract: Causal graphical models can encode large amounts structural knowledge, both from the background knowledge of domain experts and the structural knowledge discovered from randomized experiments or observational data.

By Katherine Avery, Chinmay Pendse, David Jensen

reinforcement-learning

arXiv Machine Learning

Aug 4

Dominant Arm Identification with Mixing and Recycling Observed Samples

arXiv:2608. 01545v1 Announce Type: cross Abstract: We study the problem of identifying the dominant arm in multi-armed bandits, where the objective is to find the action with the highest probability of exceeding the realized rewards of all other actions.

By Jonghyun Sim, Wonyoung Kim

arXiv AI

Jun 19

Computational Identifiability

arXiv:2606. 19361v1 Announce Type: cross Abstract: Identification conditions describe the computability of a target query or parameter of interest as a function of the type and amount of information available.

By Lucius E. J. Bynum, Rajesh Ranganath, Kyunghyun Cho

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Information-Directed Sampling for Causal Bandits

The Minimal Search Space for Conditional Causal Bandits

Best Arm Identification with Minimal Regret

Evaluating and Learning Robust Bandit Policies Under Uncertain Causal Mechanisms

Dominant Arm Identification with Mixing and Recycling Observed Samples

Computational Identifiability