Title: Estimating average causal effects from patient trajectories Show Chinese title

Title: 估计患者轨迹的平均因果效应

Abstract: In medical practice, treatments are selected based on the expected causal effects on patient outcomes. Here, the gold standard for estimating causal effects are randomized controlled trials; however, such trials are costly and sometimes even unethical. Instead, medical practice is increasingly interested in estimating causal effects among patient (sub)groups from electronic health records, that is, observational data. In this paper, we aim at estimating the average causal effect (ACE) from observational data (patient trajectories) that are collected over time. For this, we propose DeepACE: an end-to-end deep learning model. DeepACE leverages the iterative G-computation formula to adjust for the bias induced by time-varying confounders. Moreover, we develop a novel sequential targeting procedure which ensures that DeepACE has favorable theoretical properties, i.e., is doubly robust and asymptotically efficient. To the best of our knowledge, this is the first work that proposes an end-to-end deep learning model tailored for estimating time-varying ACEs. We compare DeepACE in an extensive number of experiments, confirming that it achieves state-of-the-art performance. We further provide a case study for patients suffering from low back pain to demonstrate that DeepACE generates important and meaningful findings for clinical practice. Our work enables practitioners to develop effective treatment recommendations based on population effects. Show Chinese abstract

Abstract: 在医疗实践中，治疗选择是基于其对患者结局的预期因果效应。在这里，估计因果效应的黄金标准是随机对照试验；然而，这样的试验成本高昂，并且有时甚至在伦理上是不可行的。相反，医学实践越来越多地关注从电子健康记录（即观察性数据）中估计患者（亚）群体中的因果效应。在本文中，我们旨在从随着时间收集的观察性数据（患者轨迹）中估计平均因果效应（ACE）。为此，我们提出了DeepACE：一个端到端的深度学习模型。DeepACE利用迭代G-计算公式来调整由时间变化的混杂因素引起的偏差。此外，我们开发了一种新的序列目标处理程序，以确保DeepACE具有有利的理论特性，即双重稳健性和渐近有效性。据我们所知，这是首次提出专门用于估计时变ACE的端到端深度学习模型的工作。我们在大量的实验中对比了DeepACE，确认它达到了最先进的性能。我们进一步提供了针对患有腰痛患者的案例研究，以证明DeepACE为临床实践生成了重要且有意义的发现。我们的工作使从业人员能够根据人群效应制定有效的治疗建议。