论文标题
认知神经科学中的因果关系：概念，挑战和分布鲁棒性
Causality in cognitive neuroscience: concepts, challenges, and distributional robustness
论文作者
论文摘要
尽管概率模型描述了观察到的变量之间的依赖性结构，但因果模型进一步迈进了一步：例如，他们预测认知功能如何受到扰乱神经元活动的外部干预措施的影响。在这篇评论和观点文章中，我们在认知神经科学的背景下介绍了因果关系的概念，并回顾了从数据中推断因果关系的现有方法。因果推论是一项雄心勃勃的任务，在认知神经科学中尤其具有挑战性。我们更详细地讨论了两个困难：介入数据的稀缺性以及找到正确变量的挑战。我们认为分配鲁棒性是解决这些问题的指导原则。鲁棒性（或不变性）是因果关系方法基础的基本原则。只要这些环境使因果机制完好无损，目标变量的因果模型就会在环境或受试者之间进行泛化。因此，如果候选模型未概括，则它不包括目标变量的原因，或者基础变量不代表问题的正确粒度。从这个意义上讲，在定义相关变量时，评估普遍性可能是有用的，可以用来部分补偿缺乏介入数据。
While probabilistic models describe the dependence structure between observed variables, causal models go one step further: they predict, for example, how cognitive functions are affected by external interventions that perturb neuronal activity. In this review and perspective article, we introduce the concept of causality in the context of cognitive neuroscience and review existing methods for inferring causal relationships from data. Causal inference is an ambitious task that is particularly challenging in cognitive neuroscience. We discuss two difficulties in more detail: the scarcity of interventional data and the challenge of finding the right variables. We argue for distributional robustness as a guiding principle to tackle these problems. Robustness (or invariance) is a fundamental principle underlying causal methodology. A causal model of a target variable generalises across environments or subjects as long as these environments leave the causal mechanisms intact. Consequently, if a candidate model does not generalise, then either it does not consist of the target variable's causes or the underlying variables do not represent the correct granularity of the problem. In this sense, assessing generalisability may be useful when defining relevant variables and can be used to partially compensate for the lack of interventional data.
