Active Treatment Effect Estimation via Limited Samples

Zhiheng Zhang, Haoxiang Wang, Haoxuan Li, Zhouchen Lin
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:76225-76245, 2025.

Abstract

Designing experiments for causal effect estimation remains an enduring topic in both machine learning and statistics. While much of the existing statistical literature focuses on using central limit theorems to analyze asymptotic properties of estimators, a parallel line of research has emerged around theoretical tools that provide finite-sample error bounds, offering performance on par with—or superior to—the asymptotic approaches. These finite-sample results are especially relevant in active sampling settings where the sample size is limited (for instance, under privacy or cost constraints). In this paper, we develop a finite-sample estimator with sample complexity analysis and extend its applicability to social networks. Through simulations and real-world experiments, we show that our method achieves higher estimation accuracy with fewer samples than traditional estimators endowed with asymptotic normality and other estimators backed by finite-sample guarantees.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-zhang25bz, title = {Active Treatment Effect Estimation via Limited Samples}, author = {Zhang, Zhiheng and Wang, Haoxiang and Li, Haoxuan and Lin, Zhouchen}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {76225--76245}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/zhang25bz/zhang25bz.pdf}, url = {https://proceedings.mlr.press/v267/zhang25bz.html}, abstract = {Designing experiments for causal effect estimation remains an enduring topic in both machine learning and statistics. While much of the existing statistical literature focuses on using central limit theorems to analyze asymptotic properties of estimators, a parallel line of research has emerged around theoretical tools that provide finite-sample error bounds, offering performance on par with—or superior to—the asymptotic approaches. These finite-sample results are especially relevant in active sampling settings where the sample size is limited (for instance, under privacy or cost constraints). In this paper, we develop a finite-sample estimator with sample complexity analysis and extend its applicability to social networks. Through simulations and real-world experiments, we show that our method achieves higher estimation accuracy with fewer samples than traditional estimators endowed with asymptotic normality and other estimators backed by finite-sample guarantees.} }
Endnote
%0 Conference Paper %T Active Treatment Effect Estimation via Limited Samples %A Zhiheng Zhang %A Haoxiang Wang %A Haoxuan Li %A Zhouchen Lin %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-zhang25bz %I PMLR %P 76225--76245 %U https://proceedings.mlr.press/v267/zhang25bz.html %V 267 %X Designing experiments for causal effect estimation remains an enduring topic in both machine learning and statistics. While much of the existing statistical literature focuses on using central limit theorems to analyze asymptotic properties of estimators, a parallel line of research has emerged around theoretical tools that provide finite-sample error bounds, offering performance on par with—or superior to—the asymptotic approaches. These finite-sample results are especially relevant in active sampling settings where the sample size is limited (for instance, under privacy or cost constraints). In this paper, we develop a finite-sample estimator with sample complexity analysis and extend its applicability to social networks. Through simulations and real-world experiments, we show that our method achieves higher estimation accuracy with fewer samples than traditional estimators endowed with asymptotic normality and other estimators backed by finite-sample guarantees.
APA
Zhang, Z., Wang, H., Li, H. & Lin, Z.. (2025). Active Treatment Effect Estimation via Limited Samples. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:76225-76245 Available from https://proceedings.mlr.press/v267/zhang25bz.html.

Related Material