2022-06-28-zhang22b.md

abstract	booktitle	title	year	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
We examine the role of textual data as study units when conducting causal inference by drawing parallels between human subjects and organized texts. 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., 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.	First Conference on Causal Learning and Reasoning	Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	zhang22b	0	Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts	1026	1036	1026-1036	1026	false	Zhang, Bo and Zhang, Jiayao	given family Bo Zhang given family Jiayao Zhang	2022-06-28		Proceedings of the First Conference on Causal Learning and Reasoning	177	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v177/zhang22b/zhang22b.pdf