Enumerating Optimal Cost-Constrained Adjustment Sets

Batya Kenig

Keywords: Causal Inference, Adjustment Sets, Enumeration Algorithm

TL;DR: We develop algorithms for enumerating cost-constrained adjustment sets for causal inference, balancing estimator accuracy and measurement cost through ranked enumeration.

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.

Readers: auai.org/UAI/2025/Conference, auai.org/UAI/2025/Conference/Area_Chairs, auai.org/UAI/2025/Conference/Reviewers, auai.org/UAI/2025/Conference/Submission296/Authors, auai.org/UAI/2025/Conference/Submission296/Reproducibility_Reviewers

Submission Number: 296