Learning Hidden Causal Factors from Psychometrics Data Using Distributional Information
Legaspi, Roberto Dong, Xinshuai Zeng, Donghuo Sun, Yuewen Ikeda, Kazushi Spirtes, Peter Zhang, Kun
Legaspi, Roberto
Dong, Xinshuai
Zeng, Donghuo
Sun, Yuewen
Ikeda, Kazushi
Spirtes, Peter
Zhang, Kun
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
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Language
English
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Abstract
Understanding latent variables and their causal mechanisms is central to psychological theory, yet most latent variable
models in psychology have largely remained correlational. This work attempts to address three pivotal issues: identifying useful information from observational data that reveal latent causal factors, developing algorithms to leverage
this distributional information, ensuring the identifiability of the recovered latent factors and their causal structure. We
introduce a generalizable framework for discovering hidden causal structures from observed distributions in psychometric data. Applied to survey datasets on personality traits, teacher burnout, and multitasking behavior, our method
uncovers hidden causal factors and their intricate interactions. Additionally, our findings offer an alternative perspective
on psychometric scoring, grounded in the strength of the learned causal relations. These insights contribute to behavioral modeling and measurement and await further confirmatory studies to validate their implications for psychological
science.
Citation
A. Legaspi, R. Dong, and X. Zeng, “UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society Title Learning Hidden Causal Factors from Psychometrics Data Using Distributional Information Publication Date,” no. 0, p. 47, [Online]. Available: https://escholarship.org/uc/item/3mh1c6q2
Source
Proceedings of the Annual Meeting of the Cognitive Science Society
Conference
MICCAI 2024 Grand Challenge
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Source
MICCAI 2024 Grand Challenge
Publisher
Cognitive Science Society