We examine the role of textual data as study units when conducting causal
inference by drawing parallels between human subjects and organized texts. %in
human population research. We elaborate on key causal concepts and principles,
and expose some ambiguity and sometimes fallacies. To facilitate better framing
a causal query, we discuss two strategies: (i) shifting from immutable traits
to perceptions of them, and (ii) shifting from some abstract concept/property
to its constituent parts, i.e., adopting a constructivist perspective of an
abstract concept. We hope this article would raise the awareness of the
importance of articulating and clarifying fundamental concepts before delving
into developing methodologies when drawing causal inference using textual data.

通过绘制人类主体和有组织文本之间的平行关系，我们探讨了文本数据在进行因果推论时的作用。文章强调了关键因果概念和原则，并提出了两种策略来更好地确定因果查询问题。我们希望本文能提高人们对在使用文本数据进行因果推论时阐述和澄清基本概念的重要性的认识。

使用文本数据进行因果推断的几点思考：人类受试者和组织化文本之间的相似之处

Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts

Deep neural networks (DNNs) achieve excellent performance on standard
classification tasks. However, under image quality distortions such as blur and
noise, classification accuracy becomes poor. In this work, we compare the
performance of DNNs with human subjects on distorted images. We show that,
although DNNs perform better than or on par with humans on good quality images,
DNN performance is still much lower than human performance on distorted images.
We additionally find that there is little correlation in errors between DNNs
and human subjects. This could be an indication that the internal
representation of images are different between DNNs and the human visual
system. These comparisons with human performance could be used to guide future
development of more robust DNNs.

在图像质量失真的影响下，深度神经网络的表现远不及人类，但两者的错误率存在着较少的相关性，表明图像的内部表现在网络和人类眼中存在差异。这些与人类视觉表现的比较有助于指导未来更具鲁棒性的深度神经网络的发展。