  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.
