arXiv Machine Learning June 2, 2026 By Katherine Avery, Chinmay Pendse, David Jensen

Evaluating and Learning Robust Bandit Policies Under Uncertain Causal Mechanisms

Read the original on arXiv Machine Learning →

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.

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

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 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 Machine Learning

Jun 15

Graph Structured Combinatorial Semi-Bandit with Nonlinear Reward Associations through Separable Signals

arXiv:2606. 14650v1 Announce Type: new Abstract: The identification of optimal structures within vast arrays of interconnected data necessitates significant sampling- and computational effort.

By Christoph Bauschmann, Setareh Maghsudi

reinforcement-learning

arXiv Machine Learning

Jun 2

Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation

arXiv:2509. 03456v2 Announce Type: replace-cross Abstract: Off-policy evaluation (OPE) and off-policy learning (OPL) are foundational for decision-making in offline contextual bandits.

By Imad Aouali, Otmane Sakhi

arXiv AI

Jul 13

Correlation-Aware Contextual Bandits with Surrogate Rewards for LLM Routing

arXiv:2607. 09015v1 Announce Type: cross Abstract: We study contextual bandit problems with correlated arms and access to surrogate reward signals produced by a machine learning model, motivated by applications such as large language model (LLM) routing.

By Ajay Narayanan Sridhar, Ronak Singh, Mehrdad Mahdavi, Vijaykrishnan Narayanan

llms reinforcement-learning benchmarks

arXiv AI

Jul 7

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.

By Harsh Shrivastava, Yuta Kawakami, Junpei Komiyama, Jin Tian

reinforcement-learning

Related stories

The Minimal Search Space for Conditional Causal Bandits

Information-Directed Sampling for Causal Bandits

Graph Structured Combinatorial Semi-Bandit with Nonlinear Reward Associations through Separable Signals

Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation

Correlation-Aware Contextual Bandits with Surrogate Rewards for LLM Routing

Fixed-Confidence Best-Arm Identification for Causal Mediation Analysis