Budgeted Heterogeneous Treatment Effect Estimation

Tian Qin, Tian-Zuo Wang, Zhi-Hua Zhou
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:8693-8702, 2021.

Abstract

Heterogeneous treatment effect (HTE) estimation is receiving increasing interest due to its important applications in fields such as healthcare, economics, and education. Current HTE estimation methods generally assume the existence of abundant observational data, though the acquisition of such data can be costly. In some real scenarios, it is easy to access the pre-treatment covariates and treatment assignments, but expensive to obtain the factual outcomes. To make HTE estimation more practical, in this paper, we examine the problem of estimating HTEs with a budget constraint on observational data, aiming to obtain accurate HTE estimates with limited costs. By deriving an informative generalization bound and connecting to active learning, we propose an effective and efficient method which is validated both theoretically and empirically.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-qin21b, title = {Budgeted Heterogeneous Treatment Effect Estimation}, author = {Qin, Tian and Wang, Tian-Zuo and Zhou, Zhi-Hua}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {8693--8702}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/qin21b/qin21b.pdf}, url = {https://proceedings.mlr.press/v139/qin21b.html}, abstract = {Heterogeneous treatment effect (HTE) estimation is receiving increasing interest due to its important applications in fields such as healthcare, economics, and education. Current HTE estimation methods generally assume the existence of abundant observational data, though the acquisition of such data can be costly. In some real scenarios, it is easy to access the pre-treatment covariates and treatment assignments, but expensive to obtain the factual outcomes. To make HTE estimation more practical, in this paper, we examine the problem of estimating HTEs with a budget constraint on observational data, aiming to obtain accurate HTE estimates with limited costs. By deriving an informative generalization bound and connecting to active learning, we propose an effective and efficient method which is validated both theoretically and empirically.} }
Endnote
%0 Conference Paper %T Budgeted Heterogeneous Treatment Effect Estimation %A Tian Qin %A Tian-Zuo Wang %A Zhi-Hua Zhou %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-qin21b %I PMLR %P 8693--8702 %U https://proceedings.mlr.press/v139/qin21b.html %V 139 %X Heterogeneous treatment effect (HTE) estimation is receiving increasing interest due to its important applications in fields such as healthcare, economics, and education. Current HTE estimation methods generally assume the existence of abundant observational data, though the acquisition of such data can be costly. In some real scenarios, it is easy to access the pre-treatment covariates and treatment assignments, but expensive to obtain the factual outcomes. To make HTE estimation more practical, in this paper, we examine the problem of estimating HTEs with a budget constraint on observational data, aiming to obtain accurate HTE estimates with limited costs. By deriving an informative generalization bound and connecting to active learning, we propose an effective and efficient method which is validated both theoretically and empirically.
APA
Qin, T., Wang, T. & Zhou, Z.. (2021). Budgeted Heterogeneous Treatment Effect Estimation. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:8693-8702 Available from https://proceedings.mlr.press/v139/qin21b.html.

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