Federated Combinatorial Causal Bandits with Heterogeneous Causal Influences

Zheshun Wu, Wei Chen, Zenglin Xu, Fang Kong
Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence, PMLR 337:7504-7535, 2026.

Abstract

We explore the problem of federated combinatorial causal bandits (FedCCB), where multiple agents collaboratively select variables for intervention and gather feedback. The primary objective of FedCCB is to identify optimal interventions for each agent while minimizing the total cumulative regret associated with the target nodes. A key challenge in FedCCB stems from the inherent heterogeneity of local causal models, which often exhibit diverse causal influences. To address this challenge, we propose a novel Federated Subset-Clustered Bandit (FedSCuB) method. This method groups agents to tackle heterogeneity based on the similarity of their causal relationships with specified subsets of variables. FedSCuB incorporates an intervention-based exploration strategy to collect partial observations necessary for clustering, as well as an alternating minimization method to facilitate collaboration among agents within the same cluster. Theoretical analysis demonstrates that the proposed FedSCuB method achieves sub-linear regret and offers a better regret bound compared to baseline methods. Empirical evaluations on synthetic tasks further confirm the effectiveness and superiority of our method.

Cite this Paper

BibTeX
@InProceedings{pmlr-v337-wu26f, title = {Federated Combinatorial Causal Bandits with Heterogeneous Causal Influences}, author = {Wu, Zheshun and Chen, Wei and Xu, Zenglin and Kong, Fang}, booktitle = {Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence}, pages = {7504--7535}, year = {2026}, editor = {Perković, Emilija and Malinsky, Daniel}, volume = {337}, series = {Proceedings of Machine Learning Research}, month = {17--21 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v337/main/assets/wu26f/wu26f.pdf}, url = {https://proceedings.mlr.press/v337/wu26f.html}, abstract = {We explore the problem of federated combinatorial causal bandits (FedCCB), where multiple agents collaboratively select variables for intervention and gather feedback. The primary objective of FedCCB is to identify optimal interventions for each agent while minimizing the total cumulative regret associated with the target nodes. A key challenge in FedCCB stems from the inherent heterogeneity of local causal models, which often exhibit diverse causal influences. To address this challenge, we propose a novel Federated Subset-Clustered Bandit (FedSCuB) method. This method groups agents to tackle heterogeneity based on the similarity of their causal relationships with specified subsets of variables. FedSCuB incorporates an intervention-based exploration strategy to collect partial observations necessary for clustering, as well as an alternating minimization method to facilitate collaboration among agents within the same cluster. Theoretical analysis demonstrates that the proposed FedSCuB method achieves sub-linear regret and offers a better regret bound compared to baseline methods. Empirical evaluations on synthetic tasks further confirm the effectiveness and superiority of our method.} }
Endnote
%0 Conference Paper %T Federated Combinatorial Causal Bandits with Heterogeneous Causal Influences %A Zheshun Wu %A Wei Chen %A Zenglin Xu %A Fang Kong %B Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2026 %E Emilija Perković %E Daniel Malinsky %F pmlr-v337-wu26f %I PMLR %P 7504--7535 %U https://proceedings.mlr.press/v337/wu26f.html %V 337 %X We explore the problem of federated combinatorial causal bandits (FedCCB), where multiple agents collaboratively select variables for intervention and gather feedback. The primary objective of FedCCB is to identify optimal interventions for each agent while minimizing the total cumulative regret associated with the target nodes. A key challenge in FedCCB stems from the inherent heterogeneity of local causal models, which often exhibit diverse causal influences. To address this challenge, we propose a novel Federated Subset-Clustered Bandit (FedSCuB) method. This method groups agents to tackle heterogeneity based on the similarity of their causal relationships with specified subsets of variables. FedSCuB incorporates an intervention-based exploration strategy to collect partial observations necessary for clustering, as well as an alternating minimization method to facilitate collaboration among agents within the same cluster. Theoretical analysis demonstrates that the proposed FedSCuB method achieves sub-linear regret and offers a better regret bound compared to baseline methods. Empirical evaluations on synthetic tasks further confirm the effectiveness and superiority of our method.
APA
Wu, Z., Chen, W., Xu, Z. & Kong, F.. (2026). Federated Combinatorial Causal Bandits with Heterogeneous Causal Influences. Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 337:7504-7535 Available from https://proceedings.mlr.press/v337/wu26f.html.

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