Perturbation graphs, invariant causal prediction and causal relations in psychology.

Autor: Waldorp L; University of Amsterdam, Amsterdam, The Netherlands., Kossakowski J; University of Amsterdam, Amsterdam, The Netherlands., van der Maas HLJ; University of Amsterdam, Amsterdam, The Netherlands.
Jazyk: angličtina
Zdroj: The British journal of mathematical and statistical psychology [Br J Math Stat Psychol] 2024 Oct 21. Date of Electronic Publication: 2024 Oct 21.
DOI: 10.1111/bmsp.12361
Abstrakt: Networks (graphs) in psychology are often restricted to settings without interventions. Here we consider a framework borrowed from biology that involves multiple interventions from different contexts (observations and experiments) in a single analysis. The method is called perturbation graphs. In gene regulatory networks, the induced change in one gene is measured on all other genes in the analysis, thereby assessing possible causal relations. This is repeated for each gene in the analysis. A perturbation graph leads to the correct set of causes (not nec-essarily direct causes). Subsequent pruning of paths in the graph (called transitive reduction) should reveal direct causes. We show that transitive reduction will not in general lead to the correct underlying graph. We also show that invariant causal prediction is a generalisation of the perturbation graph method and does reveal direct causes, thereby replacing transitive re-duction. We conclude that perturbation graphs provide a promising new tool for experimental designs in psychology, and combined with invariant causal prediction make it possible to re-veal direct causes instead of causal paths. As an illustration we apply these ideas to a data set about attitudes on meat consumption and to a time series of a patient diagnosed with major depression disorder.
(© 2024 The Author(s). British Journal of Mathematical and Statistical Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)
Databáze: MEDLINE
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