Feng Zheng, Prosperi Mattia, Guo Yi, Bian Jiang
University of Florida.
Res Sq. 2023 Feb 6:rs.3.rs-2536079. doi: 10.21203/rs.3.rs-2536079/v1.
This paper proposes a new approach, Variational Temporal Deconfounder (VTD), for estimating individualized treatment effects (ITE) from longitudinal observational data, where we address the hidden confounding issues by using proxies (i.e., surrogate variables that serve for unobservable variables).
We build VTD by incorporating a variational recurrent autoencoder that learns the latent encodings of hidden confounders from observed proxies and an ITE estimation network that takes the learned hidden encodings to predict the probability of receiving treatments and potential outcomes.
We test VTD on both synthetic and real-world clinical data, and the results from synthetic data experiments demonstrate VTD's effectiveness in deconfounding by outperforming existing methods, while results from two real-world datasets (i.e., Medical Information Mart for Intensive Care version III [MIMIC-III] and the National Alzheimer's Coordinating Center [NACC] database) suggest that the performance of the VTD model outperforms existing baseline models, however, varies depending on the assumptions of underlying causal structures and availability of proxies for hidden confounders.
The VTD offers a unique solution to address the confounding bias without the "unconfoundedness" assumption when estimating the ITE from longitudinal observational data. The elimination of the requirement for the "unconfoundedness" assumption makes the VTD more versatile and practical in real-world clinical applications of personalized medicine.
本文提出了一种新方法——变分时间去混杂器（VTD），用于从纵向观测数据中估计个体治疗效果（ITE），我们通过使用代理变量（即替代不可观测变量的变量）来解决隐藏的混杂问题。
我们通过合并一个变分递归自动编码器和一个ITE估计网络来构建VTD，前者从观测到的代理变量中学习隐藏混杂因素的潜在编码，后者利用学到的隐藏编码来预测接受治疗的概率和潜在结果。
我们在合成数据和真实世界临床数据上对VTD进行了测试，合成数据实验结果表明，VTD在去混杂方面比现有方法更有效，而来自两个真实世界数据集（即重症监护医学信息集市第三版[MIMIC-III]和国家阿尔茨海默病协调中心[NACC]数据库）的结果表明，VTD模型的性能优于现有的基线模型，不过，其性能会因潜在因果结构的假设以及隐藏混杂因素代理变量的可用性而有所不同。
VTD提供了一种独特的解决方案，在从纵向观测数据估计ITE时，无需“无混杂性”假设即可解决混杂偏差问题。消除对“无混杂性”假设的要求，使得VTD在个性化医学的实际临床应用中更加通用和实用。
