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Distinguishing cause from effect in psychological research: An independence‐based approach under linear non‐Gaussian models.

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Title: Distinguishing cause from effect in psychological research: An independence‐based approach under linear non‐Gaussian models.
Authors: Shi, Dexin (AUTHOR), Zhang, Bo (AUTHOR), Wiedermann, Wolfgang (AUTHOR), Fairchild, Amanda J. (AUTHOR)
Source: British Journal of Mathematical & Statistical Psychology. Nov2025, Vol. 78 Issue 3, p965-995. 31p.
Subjects: Causal inference, Psychological research, Statistical correlation, Confounding variables, Monte Carlo method, Linear statistical models
Abstract: Distinguishing cause from effect – that is, determining whether x causes y (x → y) or, alternatively, whether y causes x (y → x) – is a primary research goal in many psychological research areas. Despite its importance, determining causal direction with observational data remains a difficult task. In this study, we introduce an independence‐based approach for causal discovery between two variables of interest under a linear non‐Gaussian model framework. We propose a two‐step algorithm based on distance correlations that provides empirical conclusions on the causal directionality of effects under realistic conditions typically seen in psychological studies, that is, in the presence of hidden confounders. The performance of the proposed algorithm is evaluated using Monte‐Carlo simulations. Findings suggest that the algorithm can effectively detect the causal direction between two variables of interest, even in the presence of weak hidden confounders. Moreover, distance correlations provide useful insights into the magnitude of hidden confounding. We provide an empirical example to demonstrate the application of our proposed approach and discuss practical implications and future directions. [ABSTRACT FROM AUTHOR]
Copyright of British Journal of Mathematical & Statistical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Distinguishing cause from effect in psychological research: An independence‐based approach under linear non‐Gaussian models.
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  Data: <searchLink fieldCode="AR" term="%22Shi%2C+Dexin%22">Shi, Dexin</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Bo%22">Zhang, Bo</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wiedermann%2C+Wolfgang%22">Wiedermann, Wolfgang</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fairchild%2C+Amanda+J%2E%22">Fairchild, Amanda J.</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Mathematical+%26+Statistical+Psychology%22">British Journal of Mathematical & Statistical Psychology</searchLink>. Nov2025, Vol. 78 Issue 3, p965-995. 31p.
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  Data: <searchLink fieldCode="DE" term="%22Causal+inference%22">Causal inference</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+research%22">Psychological research</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Confounding+variables%22">Confounding variables</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+statistical+models%22">Linear statistical models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Distinguishing cause from effect – that is, determining whether x causes y (x → y) or, alternatively, whether y causes x (y → x) – is a primary research goal in many psychological research areas. Despite its importance, determining causal direction with observational data remains a difficult task. In this study, we introduce an independence‐based approach for causal discovery between two variables of interest under a linear non‐Gaussian model framework. We propose a two‐step algorithm based on distance correlations that provides empirical conclusions on the causal directionality of effects under realistic conditions typically seen in psychological studies, that is, in the presence of hidden confounders. The performance of the proposed algorithm is evaluated using Monte‐Carlo simulations. Findings suggest that the algorithm can effectively detect the causal direction between two variables of interest, even in the presence of weak hidden confounders. Moreover, distance correlations provide useful insights into the magnitude of hidden confounding. We provide an empirical example to demonstrate the application of our proposed approach and discuss practical implications and future directions. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of British Journal of Mathematical & Statistical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1111/bmsp.12391
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      – Code: eng
        Text: English
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        PageCount: 31
        StartPage: 965
    Subjects:
      – SubjectFull: Causal inference
        Type: general
      – SubjectFull: Psychological research
        Type: general
      – SubjectFull: Statistical correlation
        Type: general
      – SubjectFull: Confounding variables
        Type: general
      – SubjectFull: Monte Carlo method
        Type: general
      – SubjectFull: Linear statistical models
        Type: general
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      – TitleFull: Distinguishing cause from effect in psychological research: An independence‐based approach under linear non‐Gaussian models.
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            – D: 01
              M: 11
              Text: Nov2025
              Type: published
              Y: 2025
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