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

Title: 从患者轨迹中估计平均因果效应

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 subgroups 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 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 medical practitioners to develop effective treatment recommendations tailored to patient subgroups. Show Chinese abstract

Abstract: 在医疗实践中，治疗方案的选择是基于对患者结果的预期因果效应。 在这里，估计因果效应的金标准是随机对照试验；然而，这类试验成本高昂，有时甚至不道德。 相反，医疗实践越来越关注从电子健康记录中估计患者亚组的因果效应，即观察性数据。 在本文中，我们的目标是从随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：一种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变混杂因素引起的偏差。 此外，我们开发了一种新颖的序列靶向过程，确保DeepACE具有有利的理论性质，即双重稳健和渐近高效。 据我们所知，这是第一项提出用于估计时变ACE的端到端深度学习模型的工作。 我们在大量实验中比较了DeepACE，证实它达到了最先进的性能。 我们进一步对患有腰痛的患者进行了案例研究，以证明DeepACE为临床实践生成了重要且有意义的结果。 我们的工作使医疗从业者能够为患者亚组制定有效的治疗建议。