Enumerating Optimal Cost-Constrained Adjustment Sets

Batya Kenig
Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, PMLR 286:2082-2100, 2025.

Abstract

Estimating causal effects from observational data is a key problem in causal inference, often addressed through covariate adjustment sets that enable unbiased estimation of interventional means. This paper tackles the challenge of finding optimal covariate adjustment sets under budget constraints, a practical concern in many applications. We present algorithms for enumerating valid and minimal adjustment sets up to a specified cost, ordered by their proximity to outcome variables, which coincides with estimator variance. Our approach builds on existing graphical criteria and extends them to accommodate budgetary considerations, providing a useful tool for addressing resource limitations.

Cite this Paper

BibTeX
@InProceedings{pmlr-v286-kenig25a, title = {Enumerating Optimal Cost-Constrained Adjustment Sets}, author = {Kenig, Batya}, booktitle = {Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence}, pages = {2082--2100}, year = {2025}, editor = {Chiappa, Silvia and Magliacane, Sara}, volume = {286}, series = {Proceedings of Machine Learning Research}, month = {21--25 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v286/main/assets/kenig25a/kenig25a.pdf}, url = {https://proceedings.mlr.press/v286/kenig25a.html}, abstract = {Estimating causal effects from observational data is a key problem in causal inference, often addressed through covariate adjustment sets that enable unbiased estimation of interventional means. This paper tackles the challenge of finding optimal covariate adjustment sets under budget constraints, a practical concern in many applications. We present algorithms for enumerating valid and minimal adjustment sets up to a specified cost, ordered by their proximity to outcome variables, which coincides with estimator variance. Our approach builds on existing graphical criteria and extends them to accommodate budgetary considerations, providing a useful tool for addressing resource limitations.} }
Endnote
%0 Conference Paper %T Enumerating Optimal Cost-Constrained Adjustment Sets %A Batya Kenig %B Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2025 %E Silvia Chiappa %E Sara Magliacane %F pmlr-v286-kenig25a %I PMLR %P 2082--2100 %U https://proceedings.mlr.press/v286/kenig25a.html %V 286 %X Estimating causal effects from observational data is a key problem in causal inference, often addressed through covariate adjustment sets that enable unbiased estimation of interventional means. This paper tackles the challenge of finding optimal covariate adjustment sets under budget constraints, a practical concern in many applications. We present algorithms for enumerating valid and minimal adjustment sets up to a specified cost, ordered by their proximity to outcome variables, which coincides with estimator variance. Our approach builds on existing graphical criteria and extends them to accommodate budgetary considerations, providing a useful tool for addressing resource limitations.
APA
Kenig, B.. (2025). Enumerating Optimal Cost-Constrained Adjustment Sets. Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 286:2082-2100 Available from https://proceedings.mlr.press/v286/kenig25a.html.

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