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.

翻译：从观察性研究中估计个体化治疗效果（ITE）是因果推断中的一个基本问题，在包括医疗保健在内的多个领域具有重要意义。然而，有限的观察数据集给可靠的ITE估计带来了挑战，因为数据必须被划分到不同治疗组以训练ITE学习器。尽管治疗组间的信息共享可以部分缓解这一问题，但目前尚缺乏一个通用的框架来实现ITE估计中的端到端信息共享。为解决这一问题，我们提出了一种基于“软权重共享”的深度学习框架来训练ITE学习器，实现了治疗组间的动态端到端信息共享。该框架补充了现有的ITE学习器，并引入了一类新的ITE学习器，称为HyperITE。我们将最先进的ITE学习器扩展为HyperITE版本，并在IHDP、ACIC-2016和Twins基准数据集上进行了评估。实验结果表明，所提出的框架降低了ITE估计误差，且对于较小的数据集效果更佳。