心理学中的扰动图、不变因果预测和因果关系。

IF 1.5 3区 心理学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Lourens Waldorp, Jolanda Kossakowski, Han L J van der Maas
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引用次数: 0

摘要

心理学中的网络(图)通常局限于没有干预的环境。在此,我们考虑借鉴生物学的一个框架,在单一分析中涉及来自不同背景(观察和实验)的多种干预。这种方法被称为扰动图。在基因调控网络中,一个基因的诱导变化会对分析中的所有其他基因进行测量,从而评估可能的因果关系。分析中的每个基因都要重复这一过程。通过扰动图可以找到正确的原因集(不一定是直接原因)。随后对图中路径的剪枝(称为反式还原)应能揭示直接原因。我们证明,反式还原一般不会得出正确的底层图。我们还证明,不变因果预测是扰动图方法的一般化,确实能揭示直接原因,从而取代反式还原法。我们的结论是,扰动图为心理学实验设计提供了一种前景广阔的新工具,它与不变因果预测相结合,可以重新揭示直接原因,而不是因果路径。作为示例,我们将这些想法应用于有关肉类消费态度的数据集和被诊断为重度抑郁症患者的时间序列。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Perturbation graphs, invariant causal prediction and causal relations in psychology.

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.

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来源期刊
CiteScore
5.00
自引率
3.80%
发文量
34
审稿时长
>12 weeks
期刊介绍: The British Journal of Mathematical and Statistical Psychology publishes articles relating to areas of psychology which have a greater mathematical or statistical aspect of their argument than is usually acceptable to other journals including: • mathematical psychology • statistics • psychometrics • decision making • psychophysics • classification • relevant areas of mathematics, computing and computer software These include articles that address substantitive psychological issues or that develop and extend techniques useful to psychologists. New models for psychological processes, new approaches to existing data, critiques of existing models and improved algorithms for estimating the parameters of a model are examples of articles which may be favoured.
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