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

Bo Zhang, Jiayao Zhang
Proceedings of the First Conference on Causal Learning and Reasoning, PMLR 177:1026-1036, 2022.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v177-zhang22b, title = {Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts}, author = {Zhang, Bo and Zhang, Jiayao}, booktitle = {Proceedings of the First Conference on Causal Learning and Reasoning}, pages = {1026--1036}, year = {2022}, editor = {Schölkopf, Bernhard and Uhler, Caroline and Zhang, Kun}, volume = {177}, series = {Proceedings of Machine Learning Research}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v177/zhang22b/zhang22b.pdf}, url = {https://proceedings.mlr.press/v177/zhang22b.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts %A Bo Zhang %A Jiayao Zhang %B Proceedings of the First Conference on Causal Learning and Reasoning %C Proceedings of Machine Learning Research %D 2022 %E Bernhard Schölkopf %E Caroline Uhler %E Kun Zhang %F pmlr-v177-zhang22b %I PMLR %P 1026--1036 %U https://proceedings.mlr.press/v177/zhang22b.html %V 177 %X 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.
APA
Zhang, B. & Zhang, J.. (2022). Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts. Proceedings of the First Conference on Causal Learning and Reasoning, in Proceedings of Machine Learning Research 177:1026-1036 Available from https://proceedings.mlr.press/v177/zhang22b.html.

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