Adversarial de-confounding in individualised treatment effects estimation

Conference item

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.

Authors: Chauhan, VK; Molaei, S; Tania, MH; Thakur, A; Zhu, T

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Proceedings of Machine Learning Research
• Host title: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics
• Volume: 206
• Pages: 837-849
• Series: Proceedings of Machine Learning Research
• Publication date: 2023-04-24
• Acceptance date: 2023-01-20
• Event title: 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023)
• Event location: Palau de Congressos, Valencia, Spain
• Event website: http://aistats.org/aistats2023/
• Event start date: 2023-04-25
• Event end date: 2023-04-27
• ISSN: 2640-3498
• Language: English