论文标题
在阿尔茨海默氏症连续体中未观察到的混杂的情况下，因果效应的估计
Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's Continuum
论文作者
论文摘要
在过去的十年中，研究由于阿尔茨海默氏症而引起的神经解剖学与认知能力下降之间的关系一直是主要的研究重点。但是，为了从观察数据中推断出因果关系而不是简单的关联，我们需要（i）表达导致图形模型认知下降的因果关系，并且（ii）确保从收集的数据中识别出兴趣的因果效应。我们从当前关于阿尔茨海默氏病连续性因果关系的临床知识中得出了因果图，并表明因果效应的可识别性需要所有混杂因素的知名度。但是，在复杂的神经影像学研究中，我们既不知道所有潜在的混杂因素，也不知道它们的数据。为了减轻这一要求，我们通过通过概率潜在因素模型得出替代混杂因素来利用多种原因之间的依赖关系。在我们的理论分析中，我们证明使用替代混杂因子可以识别神经解剖学对认知的因果作用。我们定量评估方法对半合成数据的有效性，在那里我们知道真正的因果效应，并说明了其对阿尔茨海默氏病连续体的真实数据的使用，在那里它揭示了本来会错过的重要原因。
Studying the relationship between neuroanatomy and cognitive decline due to Alzheimer's has been a major research focus in the last decade. However, to infer cause-effect relationships rather than simple associations from observational data, we need to (i) express the causal relationships leading to cognitive decline in a graphical model, and (ii) ensure the causal effect of interest is identifiable from the collected data. We derive a causal graph from the current clinical knowledge on cause and effect in the Alzheimer's disease continuum, and show that identifiability of the causal effect requires all confounders to be known and measured. However, in complex neuroimaging studies, we neither know all potential confounders nor do we have data on them. To alleviate this requirement, we leverage the dependencies among multiple causes by deriving a substitute confounder via a probabilistic latent factor model. In our theoretical analysis, we prove that using the substitute confounder enables identifiability of the causal effect of neuroanatomy on cognition. We quantitatively evaluate the effectiveness of our approach on semi-synthetic data, where we know the true causal effects, and illustrate its use on real data on the Alzheimer's disease continuum, where it reveals important causes that otherwise would have been missed.
