On Transportability for Structural Causal Bandits

Min Woo Park, Sanghack Lee
Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence, PMLR 337:5266-5288, 2026.

Abstract

The structural causal bandit (SCB) framework offers a graphical approach to identifying suboptimal actions by leveraging prior knowledge of the underlying causal structure. While theoretically appealing, there has been limited guidance on how to systematically transfer information across heterogeneous datasets from multiple environments. In this paper, we study the structural causal bandit under transportability, where prior knowledge from source environments is integrated to accelerate learning in a target deployment environment. We show that exploiting causal invariances across environments improves sample efficiency in identifying the optimal action.

Cite this Paper

BibTeX
@InProceedings{pmlr-v337-park26a, title = {On Transportability for Structural Causal Bandits}, author = {Park, Min Woo and Lee, Sanghack}, booktitle = {Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence}, pages = {5266--5288}, 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/park26a/park26a.pdf}, url = {https://proceedings.mlr.press/v337/park26a.html}, abstract = {The structural causal bandit (SCB) framework offers a graphical approach to identifying suboptimal actions by leveraging prior knowledge of the underlying causal structure. While theoretically appealing, there has been limited guidance on how to systematically transfer information across heterogeneous datasets from multiple environments. In this paper, we study the structural causal bandit under transportability, where prior knowledge from source environments is integrated to accelerate learning in a target deployment environment. We show that exploiting causal invariances across environments improves sample efficiency in identifying the optimal action.} }
Endnote
%0 Conference Paper %T On Transportability for Structural Causal Bandits %A Min Woo Park %A Sanghack Lee %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-park26a %I PMLR %P 5266--5288 %U https://proceedings.mlr.press/v337/park26a.html %V 337 %X The structural causal bandit (SCB) framework offers a graphical approach to identifying suboptimal actions by leveraging prior knowledge of the underlying causal structure. While theoretically appealing, there has been limited guidance on how to systematically transfer information across heterogeneous datasets from multiple environments. In this paper, we study the structural causal bandit under transportability, where prior knowledge from source environments is integrated to accelerate learning in a target deployment environment. We show that exploiting causal invariances across environments improves sample efficiency in identifying the optimal action.
APA
Park, M.W. & Lee, S.. (2026). On Transportability for Structural Causal Bandits. Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 337:5266-5288 Available from https://proceedings.mlr.press/v337/park26a.html.

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