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.
翻译:在医疗实践中,根据对患者结果的预期因果关系,选择治疗方法。在这里,估计因果关系的黄金标准是随机控制的试验;然而,这种试验费用昂贵,有时甚至是不道德的。相反,医疗实践越来越有兴趣从电子健康记录(即观察数据)中估计病人(子)群体之间的因果关系。在本文中,我们的目标是从长期收集的观察数据(病人轨道)中估计平均因果关系(ACE)。在这方面,我们建议深ACE:一个最终到最终的深层次学习模型。深层ACE利用迭接的G计算公式来调整时间变化的蒙骗人所引发的偏见。此外,我们开发了一个新的连续测序程序,确保深层ACE具有有利的理论属性,即,即,双倍有力和无损效率。根据我们的知识,这是首次提出一个最终到最终的深层次学习模型,用于估计时间变化的ACE。我们在大量实验中比较了迭接的G-CE计算公式,在大量实验中将迭代的G-CE计算公式用于适应时间变化的偏差者所引发的偏差。此外,我们制定了新的连续测算方法,从而能够产生有意义的实验结果,从而证明我们所进行有意义的实验的临床实验结果。