Dynamic inter-treatment information sharing for individualized treatment effects estimation

Conference item

Estimation of individualized treatment effects (ITE) from observational studies is a fundamental problem in causal inference and holds significant importance across domains, including healthcare. However, limited observational datasets pose challenges in reliable ITE estimation as data have to be split among treatment groups to train an ITE learner. While information sharing among treatment groups can partially alleviate the problem, there is currently no general framework for end-to-end information sharing in ITE estimation. To tackle this problem, we propose a deep learning framework based on ‘\textit{soft weight sharing}’ to train ITE learners, enabling \textit{dynamic end-to-end} information sharing among treatment groups. The proposed framework complements existing ITE learners, and introduces a new class of ITE learners, referred to as \textit{HyperITE}. We extend state-of-the-art ITE learners with \textit{HyperITE} versions and evaluate them on IHDP, ACIC-2016, and Twins benchmarks. Our experimental results show that the proposed framework improves ITE estimation error, with increasing effectiveness for smaller datasets.

Authors: Chauhan, VK; Ghosheh, G; Clifton, DA; Molaei, S; Zhou, J

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: PMLR
• Host title: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics
• Pages: 3529-3537
• Series: Proceedings of Machine Learning Research
• Series number: 238
• Publication date: 2024-05-11
• Acceptance date: 2024-01-20
• Event title: 27th International Conference on Artificial Intelligence and Statistics (AISTATS)
• Event location: Valencia, Spain
• Event website: https://aistats.org/aistats2024/index.html
• Event start date: 2024-05-02
• Event end date: 2024-05-04
• Language: English
• Keywords: deep learning; information sharing; conditional average treatment effects; hypernetworks; transfer learning