Estimating treatment effects, especially individualized treatment effects (ITE), using observational data is challenging due to the complex situations of confounding bias. Existing approaches for estimating treatment effects from longitudinal observational data are usually built upon a strong assumption of "unconfoundedness", which is hard to fulfill in real-world practice. In this paper, we propose the Variational Temporal Deconfounder (VTD), an approach that leverages deep variational embeddings in the longitudinal setting using proxies (i.e., surrogate variables that serve for unobservable variables). Specifically, VTD leverages observed proxies to learn a hidden embedding that reflects the true hidden confounders in the observational data. As such, our VTD method does not rely on the "unconfoundedness" assumption. We test our VTD method on both synthetic and real-world clinical data, and the results show that our approach is effective when hidden confounding is the leading bias compared to other existing models.

翻译：使用观察数据估计治疗效果,特别是个性化治疗效果(ITE),由于偏差的复杂情况,使用观察数据是具有挑战性的。 估计纵向观察数据的治疗效果的现有方法通常建立在“无根据性”的有力假设基础上,而这种假设在现实世界中很难实现。 在本文中,我们提议采用“变化时间偏差(VTD)”方法,这种方法利用代理人(即用于不可观察变量的代位变量)在纵向环境中利用深度变异性嵌入。 具体地说,VTD利用观察到的代理人学习隐藏的嵌入,以反映出观测数据中真实隐藏的汇合者。因此,我们的VTD方法并不依赖“无根据性”的假设。我们用合成和现实世界的临床数据测试我们的VTD方法,结果表明,我们的方法在隐性混淆是与其他现有模型相比的主要偏差时是有效的。