Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction

Undral Byambadalai, Tatsushi Oka, Shota Yasui
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:5082-5113, 2024.

Abstract

We propose a novel regression adjustment method designed for estimating distributional treatment effect parameters in randomized experiments. Randomized experiments have been extensively used to estimate treatment effects in various scientific fields. However, to gain deeper insights, it is essential to estimate distributional treatment effects rather than relying solely on average effects. Our approach incorporates pre-treatment covariates into a distributional regression framework, utilizing machine learning techniques to improve the precision of distributional treatment effect estimators. The proposed approach can be readily implemented with off-the-shelf machine learning methods and remains valid as long as the nuisance components are reasonably well estimated. Also, we establish the asymptotic properties of the proposed estimator and present a uniformly valid inference method. Through simulation results and real data analysis, we demonstrate the effectiveness of integrating machine learning techniques in reducing the variance of distributional treatment effect estimators in finite samples.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-byambadalai24a, title = {Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction}, author = {Byambadalai, Undral and Oka, Tatsushi and Yasui, Shota}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {5082--5113}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/byambadalai24a/byambadalai24a.pdf}, url = {https://proceedings.mlr.press/v235/byambadalai24a.html}, abstract = {We propose a novel regression adjustment method designed for estimating distributional treatment effect parameters in randomized experiments. Randomized experiments have been extensively used to estimate treatment effects in various scientific fields. However, to gain deeper insights, it is essential to estimate distributional treatment effects rather than relying solely on average effects. Our approach incorporates pre-treatment covariates into a distributional regression framework, utilizing machine learning techniques to improve the precision of distributional treatment effect estimators. The proposed approach can be readily implemented with off-the-shelf machine learning methods and remains valid as long as the nuisance components are reasonably well estimated. Also, we establish the asymptotic properties of the proposed estimator and present a uniformly valid inference method. Through simulation results and real data analysis, we demonstrate the effectiveness of integrating machine learning techniques in reducing the variance of distributional treatment effect estimators in finite samples.} }
Endnote
%0 Conference Paper %T Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction %A Undral Byambadalai %A Tatsushi Oka %A Shota Yasui %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-byambadalai24a %I PMLR %P 5082--5113 %U https://proceedings.mlr.press/v235/byambadalai24a.html %V 235 %X We propose a novel regression adjustment method designed for estimating distributional treatment effect parameters in randomized experiments. Randomized experiments have been extensively used to estimate treatment effects in various scientific fields. However, to gain deeper insights, it is essential to estimate distributional treatment effects rather than relying solely on average effects. Our approach incorporates pre-treatment covariates into a distributional regression framework, utilizing machine learning techniques to improve the precision of distributional treatment effect estimators. The proposed approach can be readily implemented with off-the-shelf machine learning methods and remains valid as long as the nuisance components are reasonably well estimated. Also, we establish the asymptotic properties of the proposed estimator and present a uniformly valid inference method. Through simulation results and real data analysis, we demonstrate the effectiveness of integrating machine learning techniques in reducing the variance of distributional treatment effect estimators in finite samples.
APA
Byambadalai, U., Oka, T. & Yasui, S.. (2024). Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:5082-5113 Available from https://proceedings.mlr.press/v235/byambadalai24a.html.

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