2023/05/25 by Vinod Kumar Chauhan, Chauhan, Vinod Kumar, Jian Zhou +7 · 2 citations
Computer Science · Mathematics · Medicine · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Heart Failure Treatment and Management #Machine Learning (cs.LG) #Machine Learning in Healthcare #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2305.15984
openalex publication_date 2023/05/25 · openalex created_date 2023/05/27 · openalex updated_date 2026/07/28
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 `soft weight sharing' to train ITE learners, enabling 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 HyperITE. We extend state-of-the-art ITE learners with 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.