Waldorp Lourens, Kossakowski Jolanda, van der Maas Han L J
University of Amsterdam, Amsterdam, The Netherlands.
Br J Math Stat Psychol. 2025 Feb;78(1):303-340. doi: 10.1111/bmsp.12361. Epub 2024 Oct 21.
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.
心理学中的网络（图）通常局限于无干预的情境。在此，我们考虑一种借鉴自生物学的框架，该框架在单一分析中涉及来自不同情境（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，对分析中的一个基因的诱导变化会在所有其他基因上进行测量，从而评估可能的因果关系。对分析中的每个基因都重复此操作。一个扰动图会得出正确的因果集（不一定是直接原因）。随后对图中的路径进行修剪（称为传递简约）应能揭示直接原因。我们表明，传递简约通常不会得出正确的基础图。我们还表明，不变因果预测是扰动图方法的一种推广，并且确实能揭示直接原因，从而取代传递简约。我们得出结论，扰动图为心理学实验设计提供了一种有前景的新工具，并且与不变因果预测相结合能够揭示直接原因而非因果路径。作为例证，我们将这些想法应用于一个关于肉类消费态度的数据集以及一位被诊断患有重度抑郁症患者的时间序列。
