Adversarial De-confounding in Individualised Treatment Effects Estimation

Vinod K. Chauhan, Soheila Molaei, Marzia Hoque Tania, Anshul Thakur, Tingting Zhu, David A. Clifton
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:837-849, 2023.

Abstract

Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc. In observational studies, de-confounding is a fundamental problem of individualised treatment effects (ITE) estimation. This paper proposes disentangled representations with adversarial training to selectively balance the confounders in the binary treatment setting for the ITE estimation. The adversarial training of treatment policy selectively encourages treatment-agnostic balanced representations for the confounders and helps to estimate the ITE in the observational studies via counterfactual inference. Empirical results on synthetic and real-world datasets, with varying degrees of confounding, prove that our proposed approach improves the state-of-the-art methods in achieving lower error in the ITE estimation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v206-chauhan23a, title = {Adversarial De-confounding in Individualised Treatment Effects Estimation}, author = {Chauhan, Vinod K. and Molaei, Soheila and Tania, Marzia Hoque and Thakur, Anshul and Zhu, Tingting and Clifton, David A.}, booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics}, pages = {837--849}, year = {2023}, editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem}, volume = {206}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v206/chauhan23a/chauhan23a.pdf}, url = {https://proceedings.mlr.press/v206/chauhan23a.html}, abstract = {Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc. In observational studies, de-confounding is a fundamental problem of individualised treatment effects (ITE) estimation. This paper proposes disentangled representations with adversarial training to selectively balance the confounders in the binary treatment setting for the ITE estimation. The adversarial training of treatment policy selectively encourages treatment-agnostic balanced representations for the confounders and helps to estimate the ITE in the observational studies via counterfactual inference. Empirical results on synthetic and real-world datasets, with varying degrees of confounding, prove that our proposed approach improves the state-of-the-art methods in achieving lower error in the ITE estimation.} }
Endnote
%0 Conference Paper %T Adversarial De-confounding in Individualised Treatment Effects Estimation %A Vinod K. Chauhan %A Soheila Molaei %A Marzia Hoque Tania %A Anshul Thakur %A Tingting Zhu %A David A. Clifton %B Proceedings of The 26th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2023 %E Francisco Ruiz %E Jennifer Dy %E Jan-Willem van de Meent %F pmlr-v206-chauhan23a %I PMLR %P 837--849 %U https://proceedings.mlr.press/v206/chauhan23a.html %V 206 %X Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc. In observational studies, de-confounding is a fundamental problem of individualised treatment effects (ITE) estimation. This paper proposes disentangled representations with adversarial training to selectively balance the confounders in the binary treatment setting for the ITE estimation. The adversarial training of treatment policy selectively encourages treatment-agnostic balanced representations for the confounders and helps to estimate the ITE in the observational studies via counterfactual inference. Empirical results on synthetic and real-world datasets, with varying degrees of confounding, prove that our proposed approach improves the state-of-the-art methods in achieving lower error in the ITE estimation.
APA
Chauhan, V.K., Molaei, S., Tania, M.H., Thakur, A., Zhu, T. & Clifton, D.A.. (2023). Adversarial De-confounding in Individualised Treatment Effects Estimation. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:837-849 Available from https://proceedings.mlr.press/v206/chauhan23a.html.

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