Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective
Wen, Hechuan Chen, Tong Gong, Mingming Chai, Li Kheng Sadiq, Shazia Wasim Yin, Hongzhi
Wen, Hechuan
Chen, Tong
Gong, Mingming
Chai, Li Kheng
Sadiq, Shazia Wasim
Yin, Hongzhi
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
Organizational Units
Journal Issue
Abstract
Although numerous complex algorithms for treatment effect estimation have been developed in recent years, their effectiveness remains limited when handling insufficiently labeled training sets due to the high cost of labeling the post-treatment effect, e.g., the expensive tumor imaging or biopsy procedures needed to evaluate treatment effects. Therefore, it becomes essential to actively incorporate more high-quality labeled data, all while adhering to a constrained labeling budget. To enable data-efficient treatment effect estimation, we formalize the problem through rigorous theoretical analysis within the active learning context, where the derived key measures – factual and counterfactual covering radii determine the risk upper bound. To reduce the bound, we propose a greedy radius reduction algorithm, which excels under an idealized, balanced data distribution. To generalize to more realistic data distributions, we further propose FCCM, which transforms the optimization objective into the Factual and Counterfactual Coverage Maximization to ensure effective radius reduction during data acquisition. Furthermore, benchmarking FCCM against other baselines demonstrates its superiority across both fully synthetic and semi-synthetic datasets. Code: https://github.com/uqhwen2/FCCM.
Citation
H. Wen, T. Chen, M. Gong, L. K. Chai, S. Sadiq, and H. Yin, “Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective,” Oct. 06, 2025, PMLR. [Online]. Available: https://proceedings.mlr.press/v267/wen25b.html
Source
Proceedings of Machine Learning Research
Conference
42nd International Conference on Machine Learning, ICML 2025
Keywords
Subjects
Source
42nd International Conference on Machine Learning, ICML 2025
Publisher
ML Research Press