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：从观测性研究中估计个性化治疗效果是个体因果推断中的基本问题，在医疗保健等领域具有重要价值。然而，有限的观测数据集给可靠的个性化治疗效果估计带来挑战，因为数据必须被划分为多个治疗组来训练个性化治疗效果学习器。虽然治疗组间的信息共享可以部分缓解该问题，但目前尚缺乏通用的端到端信息共享框架。为解决此问题，本文提出基于“软权重共享”的深度学习框架来训练个性化治疗效果学习器，实现治疗组间的动态端到端信息共享。该框架是对现有个性化治疗效果学习器的补充，并引入了一类新的个性化治疗效果学习器，称为HyperITE。我们通过HyperITE版本扩展了最先进的个性化治疗效果学习器，并在IHDP、ACIC-2016和Twins基准测试中进行了评估。实验结果表明，所提出的框架能够降低个性化治疗效果估计误差，且对较小数据集的效果提升更为显著。